Gut Microbiota Mediates Dietary Modulation of Mild Cognitive Impairment in the Elderly: A Cross-Sectional Study

Gut Microbiota Mediates Dietary Modulation of Mild Cognitive Impairment in the Elderly: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gut Microbiota Mediates Dietary Modulation of Mild Cognitive Impairment in the Elderly: A Cross-Sectional Study Hongyu Zhao, Lixiang Li, Liming Zhang, Xinpeng Li, Shichen Fu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7011889/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Prospective studies investigating the relationship between dietary factors and cognitive function in elderly Chinese populations remain limited. And the role of gut microbiota in this relationship is unclear. In this study, we aimed to explore the role of gut microbiota in the dietary modulation on mild cognitive impairment (MCI). Methods This study enrolled 48 patients with MCI and 43 age-matched healthy controls (HC). Participant demographics and blood lipid levels were recorded. Dietary habits were assessed using a food frequency questionnaire (FFQ), and cognitive function was evaluated with the Montreal Cognitive Assessment (MoCA). Fecal samples were collected for 16S rRNA gene sequencing. Spearman’s correlation analysis was employed to examine correlations between gut microbiota and dietary intake, cognitive function, and low-density lipoprotein cholesterol (LDL-C). Results Compared to HC, MCI subjects had significantly lower education levels and higher serum total cholesterol (TC) and LDL-C levels ( P < 0.05). The MCI group also exhibited significantly reduced consumption of bean curd/tofu pudding, yogurt, freshwater fish, shrimp and crab, pine nuts, and pumpkin ( P < 0.05). Significant enrichment of the genera Desulfovibrio , Sutterella , UCG-003, norank_f__norank_o__RF39 , UCG-002, F0332 , Phocea , norank_f__norank_o__Elsterales , and Bryobacte was observed in the MCI group. Conversely, Actinomyces , Atopobium , Eubacterium_eligens_group , Ruminococcus_gnavus_group , and Streptococcus were significantly decreased in MCI subjects ( P < 0.05). Spearman’s correlation analysis revealed significant positive associations between cognitive scores and the intake of yogurt, freshwater fish, shrimp and crab, and pine nuts ( P < 0.05). Furthermore, Actinomyces and Streptococcus abundance correlated positively not only with the intake of freshwater fish and yogurt but also with cognitive performance. Conversely, UCG-003 and Desulfovibrio abundance correlated negatively with the intake of shrimp and crab, yogurt, as well as with cognitive scores. Additionally, serum LDL-C levels correlated negatively with yogurt intake and cognitive scores. Conclusions In conclusion, intake of yogurt, freshwater fish, and shrimp and crab was positively associated with cognitive performance. Gut microbiota composition, particularly enrichment of Actinomyces and Streptococcus , may mediate the beneficial cognitive effects of these dietary components. Conversely, UCG-003 and Desulfovibrio may exert detrimental effects on cognition. Notably, serum LDL-C levels may represent a mediating factor in the diet-cognition relationship. Diet Gut microbiota Mild cognitive impairment Low-density lipoprotein cholesterol Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Mild cognitive impairment (MCI) refers to a decline in cognitive function that is not consistent with the individual's age or education level, potentially representing an early stage of Alzheimer's disease (AD)[1]. With the global prevalence of AD demonstrating a persistent upward trajectory[2], this neurocognitive condition poses growing societal and healthcare challenges. Early identification and intervention of MCI may prevent or delay the onset of dementia[3], However, the underlying mechanisms leading to MCI remain poorly understood. Therefore, it is crucial to identify controllable factors that may contribute to the development of MCI. Emerging evidence substantiates significant associations between nutritional factors and MCI[2, 4, 5], with particular emphasis on differential impacts of different foods and dietary patterns on cognitive function[6, 7]. It has been proven that cognitive function is primarily influenced by long-term dietary habits[4, 7], and comprehensive lifestyle changes including diet may significantly improve cognition function after 20 weeks[8]. Several studies have demonstrated that control subjects exhibited significantly higher consumption of fish and eggs compared to MCI subjects[9-11], and higher intake of fish and eggs might reduce risk for cognitive decline[10, 11]. Besides, plant protein intake was correlated with better cognitive score and and a diet rich in nuts, vegetables, and fruits might be shown to improve cognitive ability in adults[7, 12]. Accumulating evidence indicates that synergistic dietary patterns integrating foods and nutrients demonstrate superior neuroprotective effects compared to isolated nutritional components, with the Mediterranean diet (MeD) representing the most extensively investigated paradigm[2, 4, 6]. The (MeD) is a dietary pattern characterized by abundant intake of plant-based foods including fruits, vegetables, whole grains, legumes, nuts, olive oil and moderate wine consumption. It incorporates daily consumption of fermented dairy products, while emphasizing seafood over other animal proteins[6, 13]. Recent studies have shown that the (MeD) and DASH(Dietary Approaches to Stop Hypertension) are correlated with a low likelihood of cognitive impairment[13-15]. However, the consumption of cholesterol-rich foods may result in elevated serum cholesterol levels, and in a specific population, increased dietary or serum cholesterol has been positively correlated with an accelerated decline in cognitive function[16]. Above all, it is suggested that dietary factors are involved in the development of MCI, but the precise mechanisms underlying diet-mediated cognitive modulation remain poorly understood. There is a growing belief that intricate interactions exist between diet and gut microbiota, with the composition of the microbiome being principally determined by dietary factors[17, 18]. It was proved that long-time diet could regulation of gut microbes[19], and was closely associated with the anti-inflammatory or pro-inflammatory products of gut microbiome[20]. Moreover, emerging evidence delineates precise nutrient-microbiota interactions, with prior study demonstrating that daily yogurt consumption induces transient probiotic ( S. thermophilus and B. lactis ) enrichment[21], and intake of oily fish, fruit, nuts, vegetables legumes and cereals has been linked to an increased abundance of Faecalibacterium prausnitzii or Roseburia hominis [20]. The intestinal microbes may utilize ingested nutrients for survival, and microbial metabolic activities significantly influence host physiology[5, 17]. Furthermore, recent studies have shown that the gut microbiota has a definite association with cognitive decline[2, 5], and Bacteroides was found to be independently increased in patients with MCI[22]. In addition, recent studies have revealed that features of gut microbiota and very low-density lipoprotein (VLDL) are associated with a cluster of diseases, including coronary artery disease and cognitive impairment[23]. Notably, animal study demonstrated that Akkermansia muciniphila effectively ameliorates high-fat, high-cholesterol (HFHC) diet-induced cognitive deficits[24]. Therefore, we presumed that gut microbiota might serve as a critical mediator bridging dietary exposures and MCI pathogenesis. In this study, we used food frequency questionnaire (FFQ) to reflect the habit of long-term diet[19], and the Montreal Cognitive Assessment (MoCA) to measure the cognitive function. Additionally, we examined the roles of gut microbiota and to clarify the underlying mechanism that connects the diet with cognitive function. Methods Study design and patients This cross-sectional study investigated diet-microbiota-cognition interactions in elderly inpatients at Qilu Hospital. We planned to enroll 100 participants (50 with MCI and 50 age-matched HC). Eligible inpatients aged 55-70 years were recruited between September 2020 and December 2021. Comprehensive clinical data were collected, including gender, age, education level, lipid profiles, and hemoglobin levels. Dietary habits were assessed using a FFQ, and cognitive function was evaluated using the MoCA. Fecal samples were collected for 16S rRNA gene sequencing. Spearman’s correlation analysis assessed correlations between gut microbiota abundance and dietary intake, cognitive scores, and low-density lipoprotein cholesterol (LDL-C). Strict exclusion criteria applied: 1. No history of chronic diseases, such as ischemic heart disease, diabetes mellitus, liver disease, kidney disease, malignant tumor, or cerebral stroke; 2. No history of alcohol abuse; 3. No history of related drug use: no antibiotic use in the last 2 months, no use of drugs that may affect lipid metabolism or antioxidant supplementation, or supplements containing a large number of fatty acids, no use of antidepressants or drugs that act on the central nervous system; 4. Subjects with AD, Parkinson’s disease (PD), or those not completing the food frequency questionnaire or cognitive tests were also excluded from the study. Dietary Assessment A FFQ was used to collect dietary information covering the past one to two years by mobile phone. This questionnaire was adapted from Nutrition and Health Surveillance of the Chinese Population in 2010 (CNHS2010-F). The FFQ comprised 15 categories (rice and whole grains, legumes and legume products, snacks, milk and dairy products, eggs, alcohol, meats and poultry, fish, fruits, nuts and seeds, fungi and seaweeds, vegetables, starch and tuber crop products, pickles, tea, and beverages), which included a total of 117 items (see table S). Each participant was questioned by a trained nutritionist regarding the frequency (per month, week, or day) and quantity of each item, which were subsequently used to estimate the average daily intake of each food. Cognitive assessment The cognitive function of each participant was assessed by two professional neurologists using the MoCA (Beijing version, www.mocatest.org)[25]. In developed countries, the commonly used cut-off score for screening MCI is 25/26. However, a previous study among Chinese elderly individuals indicated that the cut-off scores varied based on educational level: 13/14 for those with no formal education, 18/19 for those with 1–6 years of education, and 24/25 for those with 7 or more years of education[9]. These adjusted criteria for MCI have been shown to exhibit high sensitivity and specificity. Fecal microbiome analysis Firstly, the morning fecal of each participant was collected in two sterile containers and brought to the laboratory within two hour. All the fecal samples were preserved at−80 °C until processing. The 16s rRNA sequencing was performed by Majorbio (Shanghai, China). Dereplication, discard of singletons (no less than 5 reads in at least 3 samples) and rarefaction based on the minimal number of reads among samples were conducted before analysis. Ace index and Chao index were calculated to evaluate microbial richness in each sample, and Shannon index, Simpson index for alpha-diversity. Principal Coordinates Analysis (PCoA) at the level of operational taxonomy unit (OTU) was used to reveal the dissimilarities of gut microbiota between faecal samples from control subjects and MCI subjects Hierarchical clustering and PCoA on a level of all OTUs were performed based on bray-curtis distances. ANOSIM based on Bray-Curtis distance and 999 times of permutation tests was utilised to analyse structural difference between MCI and HC groups via R. LEfSe calculation was conducted from phylum to genus level between two groups at the LDA score threshold of 2.5. Statistical Analysis We utilized the C programming language to analyze FFQ data using formulas, calculating the daily intake of each food. The clinical and FFQ data from two groups were compared using SPSS22.0. The normality of the measurement data was verified with the Kolmogorov-Smirnov test and expressed as mean ± standard deviation (SD). An independent samples t-test was employed for comparisons between groups; for measurement data with a skewed distribution, expressed as median (Q1, Q3), the Mann-Whitney U test was used for inter-group comparisons. Additionally, the chi-square test was applied to analyze binary categorical variables between the normal and MCI groups. A P-value of less than 0.05 was considered statistically significant. We utilized Spearman correlation analysis to investigate the relationships between various dietary components and MoCA scores, between different dietary components and gut microbiota diversity, between gut microbiota diversity and MoCA scores, and between blood lipid levels and MoCA scores across all participants. Additionally, we conducted partial correlation analysis, treating MoCA scores as a covariate, to examine the relationship between diet and levels of total cholesterol (TC) or LDL-C. In the subgroup with MCI, we employed Spearman correlation analysis to explore the association between LDL-C and gut microbiota composition. Results Demographic Characteristics and clinical parameters In total, 91 Chinese adults (43 HC and 48 MCI subjects) were recruited in the present study (7 HC and 2 MCI subjects were removed from the group due to unqualified stool). This cohort (n=91) is statistically adequate for detecting clinically relevant microbiota-cognition relationships while enabling deep mechanistic exploration. The demographic characteristics of the participants are listed in Table 1. The MCI subjects had a lower education level and got a lower scores of MoCA than the control subjects ( P < 0.05). And participants in control group had a lower lever of total cholesterol and low density lipoprotein cholesterol than the MCI subjects( P 0.05). Food intake As shown in Table 2, the daily intake of 15 food groups had no statistical differences between control subjects and MCI subjects. Despite no statistical significance, the daily intake of milk and dairy products or fish and marine lives in the control subjects was higher than that in MCI subjects. Besides in the control subjects, the daily intake of bean curd and tofu pudding, other dried beans and products, yogurt, freshwater fish, shrim and crab, pine nut, pumpkin and cucumber preserved with soy paste was higher than that in MCI groups ( P < 0.05). The gut microbiome Stool samples were collected from 43 control subjects and 48 MCI subjects. To determine the diferences in overall gut microbiota diversity between two groups, the alpha and beta diversities were evaluated. Ace and Chao indices were used to characterize bacterial abundance within the groups. These indices revealed had a signifcant difference between the two groups ( P = 0.025, P = 0.034, respectively, Fig.1A, B). The beta diversity based on PCoA analysis did not show dramatically different clustering in two groups ( P = 0.46, Fig.1C) Using sequencing analysis, the composition of the gut microbiota was determined (Fig.1D-F). Among the seven most common bacterial communities, Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria were the main five phyla present in the gut microbiota of the two groups. Although there were no significant differences between the two groups at phylum level, there was a trend of increasing the relative abundance of Bacteroidetes in the MCI groups compared to the normal group (P=0.095) (Fig.1D). The relative abundance of Firmicutes to Bacteroidetes had no difference in two group. At family level the abundance of Oscillospiraceae, norank_o__RF39, norank_o__Elsterales, Bryobacteraceae increased in MCI subjects while Actinomycetaceae decreased ( P < 0.05, Fig. 1D, 2A). Besides, comparison at the genus level, UCG-002, norank_f__norank_o__RF39, UCG-003, Sutterella, F0332, Phocea, norank_f__norank_o__Elsterales and Bryobacter increased in MCI group and Bradyrhizobium only was found in MCI group, but Actinomyces and Atopobium increased in control group ( P < 0.05, Fig.1F, 2B). LEfSe analysis was performed to measure diferences in taxa in gut microbiota in MCI and normal groups (Fig.2C). LDA comparison at the genus level showed that MCI group had increased Coprococcus , Desulfovibrio , Sutterella , UCG-003 and norank_f__norank_o__RF39 but decreased Eubacterium _ eligens _group, Ruminococcus _ gnavus _group and Streptococcus compared to the HC group ( P < 0.05). Association between Diet and Cognitive Function As shown in figure 3, in the 15 food groups, spearman’s correlation analysis showed that the daily intake of milk and dairy products or fish and marine lives by all subjects was positively correlated with the MoCA score (r = 0.249 or 0.281, P < 0.05; n = 91; Fig.3A); in the 117 food items, the daily intake of cream was positively associated with the MoCA score(r = 0.210, P < 0.05; n = 91; Fig.3B,3C), as was freshwater fish, pine nut or preserved szechuan pickle(r = 0.213, 0.269 or 0.213, P < 0.05; n = 91; Fig.3B,3C), moreover, the daily intake of yogurt or shrim and crab had a significant positive correlation with the MoCA score (r = 0.396 or 0.294, P < 0.01; n = 91; Fig.3B,C),but other intake foods with differences between the two groups only had a trend to positive correlation with the MoCA score(Fig.3B,C). In the MCI group, the daily intake of cooked rice was positively associated with the MoCA score (r = 0.317, P < 0.05; n = 48; Fig.3D), so was cooked rice with other grains, oatmeal, freshwater fish, orange, coriander, cabbage, tomato, carrot or preserved szechuan pickle(r = 0.285, 0.308, 0.287, 0.368, 0.415, 0.345, 0.432, 0.360 or 0.336, P < 0.05; n = 48; Fig.3D), and the daily intake of yogurt or shrim and crab had a trend to positive correlation with the MoCA score(r = 0.248 or 0.281, P = 0.090 or 0.053; n = 48; Fig.3D), but the daily intake of chicken or Chinese cabbage was negatively associated with the MoCA score (r = -0.387 or -0.319, P < 0.05; n = 48; Fig.3D). The gut microbiome correlated with the diet and MoCA score We examined associations between compositions of gut microbiota and those significantly different diet using Spearman’s correlation coefficient(Fig.4A). We observed the differential bacteria in the two groups, Significant correlations were found between relative abundances of genus UCG-002 and pumpkin(r = -0.255, P = 0.0147), UCG-003 and pumpkin(r = -0.226, P = 0.0311), UCG-003 and Shrim&crab(r= -0.189, P = 0.072), Actinomyces and freshwater fish (r = 0.221, P = 0.0353), Coprococcus and pumpkin (r = -0.246, P = 0.019), Desulfovibrio and Shrim&crab (r = -0.217, P = 0.0384), Desulfovibrio and yogurt (r = -0.184, P = 0.080), Desulfovibrio and pumpkin (r = -0.181, P = 0.086), Ruminococcus_gnavus_group and pumpkin (r = 0.279, P = 0.0073), Ruminococcus_gnavus_group and yogurt(r = -0.244, P = 0.0199), Streptococcus and yogurt(r = 0.179, P = 0.0894). Beside, norank_f__norank_o__RF39 , Sutterella Eubacterium_eligens_group had no significant correlation with diet. Fourtherly, Spearman’s correlation analysis showed that relative abundances of genus Actinomyces was positively correlated with the MoCA score (r = 0.287, P = 0.0058), so as was Streptococcus , Eubacterium_hallii_group or Blautia (r = 0.254 P = 0.0151, r = 0.286, P = 0.006, r = 0.215 P = 0.041). In contrast, the negative correlation was found between relative abundances of genus Negativibacillus with the MoCA score (r = -0.233, P = 0.0261), Sutterella with the MoCA score (r = -0.232, P = 0.0268), UCG-003 with the MoCA score (r = -0.359, P = 0.0004), norank_f__norank_o__RF39 with the MoCA score (r = -0.232, P = 0.0266), Desulfovibrio with the MoCA score (r = -0.241, P = 0.0212), Alloprevotella with the MoCA score (r = -0.214, P = 0.0413), NK4A214_group with the MoCA score (r = -0.220, P = 0.0365), Coprococcus with the MoCA score (r = -0.189, P = 0.0732).Besides, Actinomyces , Streptococcus , Sutterella , UCG-003, norank_f__norank_o__RF39 or Desulfovibrio had no significant correlation with age or BMI (Fig.4B). The TC or LDL-C correlated with the diet, MoCA score and the gut microbiome. Using a partial correlation to Analyze the association between significantly different diet with the level of TC or LDL-C in the serum, we obtained a negative correlation between yogurt and the level of TC(r = -0.257, P = 0.012) or LDL-C(r = -0.262, P = 0.013) in serum by treating MocA as a control variable. Using spearman’s correlation analysis，we got that the level of TC or LDL-C in the serum is negatively with MoCA score(r = -0.1339, P = 0.2162 Fig.5A; r = -0.2502, P = 0.0194 Fig.5B). Beisdes, using the spearman correlation analysis, we found that there may be a positive correlation between the level of LDL-C in the serum with the relative abundances of genus UCG-003 or Desulfovibrio or Coprococcus or norank_f__norank_o__RF39 (r = 0.137 or 0.0794 or 0.127 or 0.470, P > 0.05, Fig.5C), and a negative correlation between the level of LDL-C in the serum with the relative abundances of genus Actinomyces or Streptococcus (r = -0.143 or -0.124, P > 0.05, Fig.5C) in MCI group. Table 1 Demographic Characteristics and clinical parameters HC (n=43) MCI (n=48) P Value Age 57.07±6.62 59.17±5.23 0.095 Gender, male(%) 67.44 56.25 0.273 Education 11.92±3.15 8.55±3.27 <0.001* BMI 25.01±2.85 25.24±2.82 0.701 MoCA score 24.5(23.5,25.5) 19.5(17,21.25) <0.001* TC 4.54±0.85 4.98±1.06 0.039* TG 1.37±0.8 1.44±0.68 0.669 HDL-C 1.3±0.41 1.34±0.28 0.602 LDL-C 2.48±0.66 2.85±0.72 0.015* sdLDL 0.71±0.34 0.82±0.41 0.172 Fatty acid 48.72±19.5 48.27±16.59 0.909 Hb 142.55±13.2 142.32±14.33 0.938 BMI: body mass index; TC: total cholesterol; TG: triglyceride; HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol; sdLDL-C: small, dense low-density lipoprotein; Hb: hemoglobin. * P < 0.05 was considered to be statistically significant. Table 2 Daily intake of detailed food items in control and MCI groups. Food categorie Item HC (g/d) MCI (g/d) P value rice and whole grain 407.29(285.12,570.00) 403.21(284.36,489,48) 0.735 Legume and legume product 100.00(52.67,159.99) 84.11(45.95,148.59) 0.499 Bean curd&tofu pudding 14.29(8.57,28.57) 10.00(2.08,21.43) 0.035* Other dried beans and products 0(0,1.33) 0(0,0) 0.040* snacks 11.07(1.90,28.69) 9.14(1.25,36.35) 0.967 milk and dairy products 250.00(75.00,295.90) 236.61(35.77,311.07) 0.094 yogurt 21.42(0,53.57) 0(0,22.50) 0.012* eggs 84.67(62.33,120.00) 120.00(76.75,132.46) 0.164 alcohol 21.43(0.00,105.42) 33.66(0.00,142.12) 0.933 meats and poultry 111.67(41.91,217.62) 83.69(37.77,131.61) 0.278 fish and marine lives 37.62(16.67,61.76) 21.20(11.81,40.69) 0.064 freshwater fish 8.33(3.33,28.33) 4.5(0,14.29) 0.036* Shrim&crab 2.85(0,8.57) 1.41(0,3.55) 0.049* fruits 187.62(80.95,292.85) 210.71(71.55,300.59) 0.962 Nuts and seeds 28.76(10.31,40.64) 17.95(8.26,36.52) 0.352 pine nut 0(0,0.67) 0(0,0) 0.016* Fungi and seaweeds 30.00(11.19,61.37) 27.50(13.40,62.02) 0.753 vegetables 316.43(228.10,439.29) 323.21(237.89,426.55) 0.975 Chinese watermelon 6.67(0,14.29) 3.33(0,6.67) 0.074 pumpkin 14.29(3.33,28.57) 4.16(0,14.29) 0.032* starch and tuber crops product 5.00(1.67,14.29) 6.55(1.49,13.42) 0.759 pickles 0.64(0,5.58) 1.78(0.00,10.00) 0.458 Cucumber preserved with soy paste 0(0,2.85) 0(0,0.03) 0.043* tea and beverages 255.58(10.00,1001.43) 73.12(5.63,517.53) 0.192 Discussion Our findings reveal significant associations between cognitive preservation and consumption of dairy products (notably yogurt) and aquatic foods (freshwater fish and crustaceans), with pine nuts also exhibiting neuroprotective effects. Genus-level gut microbiota analysis revealed distinct compositional differences between MCI and control groups. Notably, Actinomyces and Streptococcus were identified as potential mediators of dietary neuroprotection, whereas UCG-003 and Desulfovibrio appeared to counteract these beneficial effects. Furthermore, serum LDL-C levels exhibited dual negative correlations with both yogurt consumption and cognitive performance, suggesting a potential link with the observed microbiota alterations. Collectively, these findings support a novel mechanistic pathway linking dietary patterns to MCI risk through gut microbiota-mediated serum LDL-C modulation, underscoring the critical role of the gut-brain axis in dietary influences on cognitive preservation. In this study, we observed that participants in the normal group tended to consume more milk and dairy products, as well as fish and marine lives, compared to those in the MCI group. Additionally, among 117 surveyed food items, the control group showed preferential intake of legumes ( bean curd and tofu pudding, other dried beans and products), yogurt, freshwater fish, crustaceans (shrimp and crab), pine nuts and pumpkin ( P <0.05) - dietary patterns aligning with established neuroprotective nutritional profiles reported in prior studies[6, 12, 26]. Existing cohort studies reveal that older adults (≥65 years) consuming one or more servings of fish per week exhibit 0.35% slower cognitive decline rates than those with lower intake[11]. Furthermore, elevated consumption of crustaceans (shrimp, scallop and lobster) and legumes (peas and lima beans) correlated with a reduced risk of subjective cognitive decline[7]. Recent evidence further identifies legumes, liquid-type yogurt and curd-type yogurt intake as significant protective factors against MCI[27]. Mechanistically, pumpkin is regarded as beneficial for brain health due to its rich content of flavonoids, phenolic compounds, vitamins, and carbohydrates[28], with animal models confirming cognitive preservation via traditional Mesoamerican diets combining pumpkin, fish, and black beans and other nutritious components[29]. Our analysis of 91 participants revealed significant positive associations between MoCA scores and consumption of two key food categories: milk and dairy products or fish and marine lives. Notably, cream, yogurt, freshwater fish, shrimp and crab or pine nut was significantly positively correlated with the cognitive function only pumpkin has a trend, indicating that certain foods might have an advantageous impact on cognitive function. Additionally, the combination of these varied foods between two groups is similar to the Mediterranean diet, which prevented the decline of cognitive function by the nutrition from fish, dairy, fruit and so on[30]. Thus, the effects of these varied foods on mild cognitive impairment may be attributed to improved nutrient intake, warranting further exploration. An increasing number of population-based studies indicate that changes in gut microbiota are closely associated with the occurrence of mild cognitive impairment[31, 32]. Previous studies have shown that abundance of phyla Bacteroidetes are significantly increased in the gut microbiota of patients with MCI and Alzheimer's disease[22, 32], but our cohort showed only abundance of phyla Bacteroidetes elevation ( P =0.095) without statistical significance versus control. In this study, comparative analysis revealed significantly higher abundances of the Actinomycetaceae family and Actinomyces genus in the normal group compared to the MCI group ( P <0.05). This finding aligns with previous reports demonstrating reduced Actinobacteria phylum abundance in Alzheimer's disease patients[33]. Notably, emerging evidence suggests a neuroprotective association, as Actinomycetaceae levels show significantly positive correlation with total MoCA scores in peritoneal dialysis patients[31]. Besides, we found that Streptococcus , Eubacterium_eligens_group and Ruminococcus_gnavus_group genus were increased in normal group compared with MCI group. Prior clinical observations by Wang and colleagues demonstrated similar Streptococcus which was positively correlated with MMSE scores enrichment in cognitively intact peritoneal dialysis patients, and Streptococcaceae and Atopobium showing positive correlations with both MMSE and MoCA scores independent of confounding age factors[31]. Moreover, animal experiments indicated that cognitive function improved in aged mice fed with blueberry-mulberry extract (BME), and the relative abundance of Streptococcus in the gut increased[34]. However, at the genus level, the MCI group exhibited marked microbial dysbiosis characterized by significant enrichment of sulfate-reducing taxa ( Desulfovibrio ), pro-inflammatory commensals ( Sutterella ), and multiple unclassified genera (UCG-002, UCG-003,etc.) compared to cognitively normal controls ( P <0.05). Critically, this dysbiotic pattern mirrors Alzheimer's-associated microbiota alterations, where Desulfovibrionales order and Desulfovibrionaceae family overabundance exhibits causal associations with AD pathogenesis[35]. While the UCG-003 genus of the Oscillospiraceae family remains understudied in AD or MCI, it has been found to be enriched in patients with acute ischemic stroke exhibiting mild to moderate neurological deficits[36]. Notably, subjective memory complaints exhibit dose-dependent relationships with Sutterella (+25% per complaint unit) and Desulfovibrionaceae (+27%)[37]. Collectively, our findings implicate Actinomyces , Streptococcus , UCG-003, Sutterella , and Desulfovibrio as potential mediators of MCI pathogenesis. Dietary modulation of gut microbial ecology directly influences host body, which might result in beneficial or detrimental consequences on host health[17, 18]. Our Spearman analysis revealed Actinomyces abundance as positively correlated with both freshwater fish intake and cognitive scores. Furthermore, yogurt consumption trended toward positive association with Streptococcus , which itself demonstrated significant correlation with cognitive levels. Emerging evidence suggests dietary modulation of cognition may occur through microbiota-dependent pathways.While Actinobacteria are abundant in freshwater ecosystems and fish microbiota[38], human gut Actinobacteria responses to fish consumption remain unexplored. Beisdes, Actinobacteria depletion manifests in AD rodent models[39] and cognitively impaired peritoneal dialysis patients, where Actinomyces abundance positively correlates with cognitive scores[31]. Similarly, yogurt consumption elevates intestinal Streptococcus , particularly neuroprotective S. thermophilus[40] , [41]. This is corroborated by Danggui Shaoyao San improving cognition in AD rats via Streptococcus enrichment[39], and S. thermophilus MN-ZLW-002 mitigating cognitive deficits in APP/PS1 mice through gut-brain axis modulation[42]. Collectively, Actinomyces and Streptococcus may emerge as key mediators of freshwater fish and yogurt neuroprotection. However, we also discovered that both pumpkin and crustacean were inversely correlated with Oscillospiraceae_UCG-003, which was significantly negatively correlated with cognitive scores. Similarly, Desulfovibrio abundance showed inverse relationships not only with crustaceans, yogurt, pumpkin but also with cognitive levels. Animal experiments found that a high-fat diet which caused cognitive dysfunction[43] altered the composition of the gut microbiota, significantly upregulated lead to Oscillospiraceae_UCG-003[44] and Desulfovibrio[45, 46]. Besides, AD models exhibit Desulfovibrio enrichment versus controls[39] and a water-soluble polysaccharide extracted from mussels reversed the increase of Desulfovibrio in immunosuppressed mice[47]. Collectively, we believe that UCG-003 and Desulfovibrio likely antagonize the cognitive benefits of pumpkin, shrimp-crab or yogurt. Previous studies found that serum TC and LDL-C levels were elevated in AD and MCI, playing a significant role in the pathophysiological processes of both conditions[48, 49]. In addtion, yogurt consumption reduced these atherogenic lipids and might ameliorate cognitive decline[50]. It has proved that Desulfovibrio is positively correlated with serum TC and LDL-C[51], and probiotic yogurt probiotic yogurt suppresses this LPS-producing genus while reducing the level of serum TG, TC, and LDL-C in HFD-fed hamsters[52]. Network meta-analysis identified the combination of Lactobacillus , Bifidobacterium , and Streptococcus as optimal for lipid-lowering efficacy[53]. corroborated by Streptococcus thermophilus fermented milk significantly lowering LDL-C in aadults with subclinical hypercholesterolemia[54]. Our analysis revealed elevated serum TC and LDL-C levels in MCI patients versus normal controls. Partial correlation analysis demonstrated significant inverse associations between yogurt consumption and both TC and LDL-C. Critically, LDL-C levels negatively correlated with MoCA scores. In MCI patients, the serum LDL-C level might be positively correlated with genus Desulfovibrio , but negatively correlated with genus Streptococcus . In summary, we conclused that consuming yogurt may affect the levels of Desulfovibrio or Streptococcus in gut, which may reduce the serum LDL-C level, resulting in improving the cognitive function. This study has several limitations. First, the small sample size is a drawback. Furthermore, we only use Spearman’s correlation to analyse the relation among diet, gut microbiota and cognitive function, so there may be some potentially uncontrolled covariates during data analysis. Therefore, a large-scale cohort study is needed to clarify the relationship between diet and cognitive performance. Moreover, the participants in this study were mainly Chinese, so our findings maybe not necessarily applicable to other populations. Conclusion Our finding reveals that the consumption of yogurt, freshwater fish, crustaceans (shrimp and crab), pine nuts, and pumpkin exerts a protective effect on cognitive function. Furthermore, Actinomyces or Streptococcus may mediate the beneficial effect of freshwater fish or yogurt on cognition, while UCG-003 or Desulfovibrio might hinder the protective effect of crustaceans or yogurt on cognition. Particularly, yogurt might decrease Desulfovibrio and increase Streptococcus , subsequently reducing LDL levels to enhance cognitive function via the microbiota-lipid-brain axis. Abbreviations Abbreviation Definition MCI Mild cognitive impairment HC Healthy controls FFQ Food frequency questionnaire MoCA Montreal Cognitive Assessment LDL-C Low-density lipoprotein cholesterol AD Alzheimer's disease MeD Mediterranean diet DASH Dietary Approaches to Stop Hypertension VLDL Very low-density lipoprotein PD Parkinson’s disease OUT Operational taxonomy unit TC Total cholesterol Declarations Ethical approval and consent to participate The procedures followed the ethical standards of the Helsinki Declaration of 1975. Written informed consent was obtained from all participants. The study protocol was approved by the Human Ethics Committee of Qilu Hospital (No. KYLL-202008-184). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Availability of data The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Funding This work was supported by Shandong Province Medical and Health Science and Technology Development Program (2018WS304 and 202103030834). Authors’ contributions Guarantor of the article: X-LZ. Specific Author contributions: H-YZ and X-LZ designed and planned the study. H-YZ and S-CF collected the data. L-XL and L-MZ performed statistical analysis. H-YZ and X-PL prepared the original draft. H-YZ and F-XC wrote the manuscript. L-XL,X-LZ and Y-QL supported editing of the original draft and critically revised the manuscript. All authors approved the final draft. Acknowledgments We thank all patients in this study and the help of the Department of Psychology at Qilu Hospital. References Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC et al: The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. ALZHEIMERS DEMENT 2011, 7(3):270-279. Dissanayaka DMS, Jayasena V, Rainey-Smith SR, Martins RN, Fernando WMAD: The Role of Diet and Gut Microbiota in Alzheimer's Disease. NUTRIENTS 2024, 16(3):412. Jongsiriyanyong S, Limpawattana P: Mild Cognitive Impairment in Clinical Practice: A Review Article. AM J ALZHEIMERS DIS 2018, 33(8):500-507. Wu J, Song X, Chen GC, Neelakantan N, van Dam RM, Feng L, Yuan JM, Pan A, Koh WP: Dietary pattern in midlife and cognitive impairment in late life: a prospective study in Chinese adults. AM J CLIN NUTR 2019, 110(4):912-920. Romanenko M, Kholin V, Koliada A, Vaiserman A: Nutrition, Gut Microbiota, and Alzheimer's Disease. FRONT PSYCHIATRY 2021, 12:712673. Scarmeas N, Anastasiou CA, Yannakoulia M: Nutrition and prevention of cognitive impairment. LANCET NEUROL 2018, 17(11):1006-1015. Yeh TS, Yuan C, Ascherio A, Rosner BA, Blacker D, Willett WC: Long-term dietary protein intake and subjective cognitive decline in US men and women. AM J CLIN NUTR 2022, 115(1):199-210. Ornish D, Madison C, Kivipelto M, Kemp C, McCulloch CE, Galasko D, Artz J, Rentz D, Lin J, Norman K et al: Effects of intensive lifestyle changes on the progression of mild cognitive impairment or early dementia due to Alzheimer's disease: a randomized, controlled clinical trial. Alzheimer's research & therapy 2024, 16(1):117-122. Yuan L, Zhen J, Ma W, Cai C, Huang X, Xiao R: The Erythrocyte Fatty Acid Profile and Cognitive Function in Old Chinese Adults. NUTRIENTS 2016, 8(7):385. Zhao X, Yuan L, Feng L, Xi Y, Yu H, Ma W, Zhang D, Xiao R: Association of dietary intake and lifestyle pattern with mild cognitive impairment in the elderly. J NUTR HEALTH AGING 2015, 19(2):164-168. Qin B, Plassman BL, Edwards LJ, Popkin BM, Adair LS, Mendez MA: Fish intake is associated with slower cognitive decline in Chinese older adults. J NUTR 2014, 144(10):1579-1585. Dong L, Xiao R, Cai C, Xu Z, Wang S, Pan L, Yuan L: Diet, lifestyle and cognitive function in old Chinese adults. ARCH GERONTOL GERIAT 2016, 63:36-42. Vinciguerra F, Graziano M, Hagnäs M, Frittitta L, Tumminia A: Influence of the Mediterranean and Ketogenic Diets on Cognitive Status and Decline: A Narrative Review. NUTRIENTS 2020, 12(4):1019. Tsivgoulis G, Judd S, Letter AJ, Alexandrov AV, Howard G, Nahab F, Unverzagt FW, Moy C, Howard VJ, Kissela B et al: Adherence to a Mediterranean diet and risk of incident cognitive impairment. NEUROLOGY 2013, 80(18):1684-1692. Duplantier SC, Gardner CD: A Critical Review of the Study of Neuroprotective Diets to Reduce Cognitive Decline. NUTRIENTS 2021, 13(7):2264. An Y, Zhang X, Wang Y, Wang Y, Liu W, Wang T, Qin Z, Xiao R: Longitudinal and nonlinear relations of dietary and Serum cholesterol in midlife with cognitive decline: results from EMCOA study. MOL NEURODEGENER 2019, 14(1):51. Gentile CL, Weir TL: The gut microbiota at the intersection of diet and human health. SCIENCE 2018, 362(6416):776-780. Zmora N, Suez J, Elinav E: You are what you eat: diet, health and the gut microbiota. NAT REV GASTRO HEPAT 2019, 16(1):35-56. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen Y, Keilbaugh SA, Bewtra M, Knights D, Walters WA, Knight R et al: Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes. Science (American Association for the Advancement of Science) 2011, 334(6052):105-108. Bolte LA, Vich VA, Imhann F, Collij V, Gacesa R, Peters V, Wijmenga C, Kurilshikov A, Campmans-Kuijpers M, Fu J et al: Long-term dietary patterns are associated with pro-inflammatory and anti-inflammatory features of the gut microbiome. GUT 2021, 70(7):1287-1298. Le Roy CI, Kurilshikov A, Leeming ER, Visconti A, Bowyer RCE, Menni C, Falchi M, Koutnikova H, Veiga P, Zhernakova A et al: Yoghurt consumption is associated with changes in the composition of the human gut microbiome and metabolome. BMC MICROBIOL 2022, 22(1). Saji N, Murotani K, Hisada T, Tsuduki T, Sugimoto T, Kimura A, Niida S, Toba K, Sakurai T: The relationship between the gut microbiome and mild cognitive impairment in patients without dementia: a cross-sectional study conducted in Japan. SCI REP-UK 2019, 9(1):19227. Xu S, Liu Y, Wang Q, Liu F, Xian Y, Xu F, Liu Y: Gut microbiota in combination with blood metabolites reveals characteristics of the disease cluster of coronary artery disease and cognitive impairment: a Mendelian randomization study. FRONT IMMUNOL 2024, 14. Higarza SG, Arboleya S, Arias JL, Gueimonde M, Arias N: Akkermansia muciniphila and environmental enrichment reverse cognitive impairment associated with high-fat high-cholesterol consumption in rats. GUT MICROBES 2021, 13(1):1-20. Lu J, Li D, Li F, Zhou A, Wang F, Zuo X, Jia XF, Song H, Jia J: Montreal cognitive assessment in detecting cognitive impairment in Chinese elderly individuals: a population-based study. J GERIATR PSYCH NEUR 2011, 24(4):184-190. Theodore LE, Kellow NJ, McNeil EA, Close EO, Coad EG, Cardoso BR: Nut Consumption for Cognitive Performance: A Systematic Review. Advances in nutrition (Bethesda, Md.) 2021, 12(3):777-792. Kim KY, Yun JM: Association between diets and mild cognitive impairment in adults aged 50 years or older. NUTR RES PRACT 2018, 12(5):415-425. Kim NR, Kim HY, Kim MH, Kim HM, Jeong HJ: Improvement of depressive behavior by Sweetme Sweet Pumpkin and its active compound, beta-carotene. LIFE SCI 2016, 147:39-45. Avila-Nava A, Noriega LG, Tovar AR, Granados O, Perez-Cruz C, Pedraza-Chaverri J, Torres N: Food combination based on a pre-hispanic Mexican diet decreases metabolic and cognitive abnormalities and gut microbiota dysbiosis caused by a sucrose-enriched high-fat diet in rats. MOL NUTR FOOD RES 2017, 61(1). Roman GC, Jackson RE, Gadhia R, Roman AN, Reis J: Mediterranean diet: The role of long-chain omega-3 fatty acids in fish; polyphenols in fruits, vegetables, cereals, coffee, tea, cacao and wine; probiotics and vitamins in prevention of stroke, age-related cognitive decline, and Alzheimer disease. REV NEUROL-FRANCE 2019, 175(10):724-741. Wang J, Wu S, Zhang J, Li Y, Wu Y, Qi X: Correlation between gut microbiome and cognitive impairment in patients undergoing peritoneal dialysis. BMC NEPHROL 2023, 24(1). Sheng C, Yang K, He B, Du W, Cai Y, Han Y: Combination of gut microbiota and plasma amyloid-beta as a potential index for identifying preclinical Alzheimer's disease: a cross-sectional analysis from the SILCODE study. ALZHEIMERS RES THER 2022, 14(1):35. Vogt NM, Kerby RL, Dill-McFarland KA, Harding SJ, Merluzzi AP, Johnson SC, Carlsson CM, Asthana S, Zetterberg H, Blennow K et al: Gut microbiome alterations in Alzheimer's disease. SCI REP-UK 2017, 7(1):13537. Li H, Xiao C, Wang F, Guo X, Zhou Z, Jiang Y: Blueberry-Mulberry Extract Alleviates Cognitive Impairment, Regulates Gut Metabolites, and Inhibits Inflammation in Aged Mice. FOODS 2023, 12(4). Guo X, Zhang X, Tang P, Chong L, Li R: Integration of genome-wide association studies (GWAS) and microbiome data highlights the impact of sulfate-reducing bacteria on Alzheimer's disease. AGE AGEING 2023, 52(7). Chou PS, Hung WC, Yang IH, Kuo CM, Wu MN, Lin TC, Fong YO, Juan CH, Lai CL: Predicting Adverse Recanalization Therapy Outcomes in Acute Ischemic Stroke Patients Using Characteristic Gut Microbiota. MICROORGANISMS 2023, 11(8). Wu F, Davey S, Clendenen TV, Koenig KL, Afanasyeva Y, Zhou B, Bedi S, Li H, Zeleniuch-Jacquotte A, Chen Y: Gut Microbiota and Subjective Memory Complaints in Older Women. J ALZHEIMERS DIS 2022, 88(1):251-262. Sadeghi J, Chaganti SR, Johnson TB, Heath DD: Host species and habitat shape fish-associated bacterial communities: phylosymbiosis between fish and their microbiome. MICROBIOME 2023, 11(1):1-258. Jin Y, Liang S, Qiu J, Jin J, Zhou Y, Huang Y, He C, Yu W, Deng S, Cheng S et al: Intestinal flora study reveals the mechanism of Danggui Shaoyao San and its decomposed recipes to improve cognitive dysfunction in the rat model of Alzheimer's disease. FRONT CELL INFECT MI 2023, 13:1323674. Kim C, Yoon L, Michels K, Tranfield W, Jacobs J, May F: The Impact of Prebiotic, Probiotic, and Synbiotic Supplements and Yogurt Consumption on the Risk of Colorectal Neoplasia among Adults: A Systematic Review. NUTRIENTS 2022, 14(22):4937. Kok CR, Hutkins R: Yogurt and other fermented foods as sources of health-promoting bacteria. NUTR REV 2018, 76(Supplement_1):4-15. Zhang Y, Wang Y, Zhou Z, Yang Y, Zhao J, Kang X, Li Z, Shen X, He F, Cheng R: Live and Heat-Inactivated Streptococcus thermophilus MN-ZLW-002 Mediate the Gut-Brain Axis, Alleviating Cognitive Dysfunction in APP/PS1 Mice. NUTRIENTS 2024, 16(6):844. Tan BL, Norhaizan ME: Effect of High-Fat Diets on Oxidative Stress, Cellular Inflammatory Response and Cognitive Function. NUTRIENTS 2019, 11(11):2579. Sun Y, Liu X, Wang R, Liu R, Lv X, Ma Y, Li Q: Lacticaseibacillus rhamnosus HF01 fermented yogurt alleviated high-fat diet-induced obesity and hepatic steatosisvia the gut microbiota–butyric acid–hepatic lipid metabolism axis. FOOD FUNCT 2024, 15(8):4475-4489. Zhai Z, Yang Y, Chen S, Wu Z: Long-Term Exposure to Polystyrene Microspheres and High-Fat Diet–Induced Obesity in Mice: Evaluating a Role for Microbiota Dysbiosis. ENVIRON HEALTH PERSP 2024, 132(9). Wang S, Huang XF, Zhang P, Wang H, Zhang Q, Yu S, Yu Y: Chronic rhein treatment improves recognition memory in high-fat diet-induced obese male mice. J NUTR BIOCHEM 2016, 36:42-50. Xiang X, Wang R, Chen L, Chen Y, Zheng B, Deng S, Liu S, Sun P, Shen G: Immunomodulatory activity of a water-soluble polysaccharide extracted from mussel on cyclophosphamide-induced immunosuppressive mice models. NPJ SCI FOOD 2022, 6(1):26. Yu Y, Yan P, Cheng G, Liu D, Xu L, Yang M, Xu H, Cheng X, Lian P, Zeng Y: Correlation between serum lipid profiles and cognitive impairment in old age: a cross-sectional study. GEN PSYCHIAT 2023, 36(2):e101009. Liu Y, Zhong X, Shen J, Jiao L, Tong J, Zhao W, Du K, Gong S, Liu M, Wei M: Elevated serum TC and LDL-C levels in Alzheimer's disease and mild cognitive impairment: A meta-analysis study. BRAIN RES 2020, 1727:146554. Pannerchelvan S, Rios-Solis L, Wasoh H, Sobri MZM, Faizal Wong FW, Mohamed MS, Mohamad R, Halim M: Functional yogurt: a comprehensive review of its nutritional composition and health benefits. FOOD FUNCT 2024, 15(22):1927-1955. Zhang X, Coker OO, Chu ES, Fu K, Lau HCH, Wang Y, Chan AWH, Wei H, Yang X, Sung JJY et al: Dietary cholesterol drives fatty liver-associated liver cancer by modulating gut microbiota and metabolites. GUT 2021, 70(4):761-774. Zhu L, Ying N, Hao L, Fu A, Ding Q, Cao F, Ren D, Han Q, Li S: Probiotic yogurt regulates gut microbiota homeostasis and alleviates hepatic steatosis and liver injury induced by high‐fat diet in golden hamsters. FOOD SCI NUTR 2024, 12(4):2488-2501. Yang Y, Yang L, Wu J, Hu J, Wan M, Bie J, Li J, Pan D, Sun G, Yang C: Optimal probiotic combinations for treating nonalcoholic fatty liver disease: A systematic review and network meta-analysis. Clinical nutrition (Edinburgh, Scotland) 2024, 43(6):1224-1239. Ito M, Kusuhara S, Yokoi W, Sato T, Ishiki H, Miida S, Matsui A, Nakamori K, Nonaka C, Miyazaki K: Streptococcus thermophilus fermented milk reduces serum MDA-LDL and blood pressure in healthy and mildly hypercholesterolaemic adults. BENEF MICROBES 2017, 8(2):171-178. Additional Declarations No competing interests reported. Supplementary Files tableS.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7011889","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488663313,"identity":"e017c6e9-3bf8-4eec-bf44-ad5f69fa91f0","order_by":0,"name":"Hongyu Zhao","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Zhao","suffix":""},{"id":488663314,"identity":"d930340a-640a-416b-9d08-cf6414f5d539","order_by":1,"name":"Lixiang Li","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Lixiang","middleName":"","lastName":"Li","suffix":""},{"id":488663315,"identity":"e9687d65-db33-4c1a-9e91-a6ea5d418a79","order_by":2,"name":"Liming Zhang","email":"","orcid":"","institution":"Zaozhuang Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liming","middleName":"","lastName":"Zhang","suffix":""},{"id":488663317,"identity":"964dac4a-de8d-4b62-96e3-96716a3b511b","order_by":3,"name":"Xinpeng Li","email":"","orcid":"","institution":"Shandong Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xinpeng","middleName":"","lastName":"Li","suffix":""},{"id":488663327,"identity":"7c0a7493-ce75-42f1-af83-5ab696bbd368","order_by":4,"name":"Shichen Fu","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Shichen","middleName":"","lastName":"Fu","suffix":""},{"id":488663328,"identity":"b1468edc-bddf-4a56-866f-5f0d596aa678","order_by":5,"name":"Feixue Chen","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Feixue","middleName":"","lastName":"Chen","suffix":""},{"id":488663329,"identity":"65def9d6-4e49-4591-92a6-3f98e8ee5adc","order_by":6,"name":"Yanqing Li","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yanqing","middleName":"","lastName":"Li","suffix":""},{"id":488663330,"identity":"5db49795-54ad-425e-aae9-50e43eb4d5fc","order_by":7,"name":"Xiuli Zuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIie3PMQuCUBDA8RMhlwPXEwq/wgtBBIO+yhNBF2kJpKHhTTa6+jVamgXByWp1rKW5sSlSoqGhh24N7z/dcD+OA1Cp/jUOHpoArBv1wYTQEqMIAAErhxK7CGu6ZjR1ztWBYOMHwjiWUqIVUciCjNBto5SgiQOBKy4lOiXzy5ugS1pWBYKQScmEElb2xMmbjjwHEPxcYZB0RAwghLeQ8RMhtdHa43XsZJjIib0La+uRLpZmXu3b+9af5UYjJ9/x/rsR+yqVSqX60QuIZTqTS1r3fwAAAABJRU5ErkJggg==","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Xiuli","middleName":"","lastName":"Zuo","suffix":""}],"badges":[],"createdAt":"2025-06-30 14:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7011889/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7011889/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87665151,"identity":"1867a69e-da31-4750-b9eb-bd0ffd531a5a","added_by":"auto","created_at":"2025-07-27 11:02:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96989,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences between MCI group and control group on gut microbia.A-B: Ace and Chao index of IBS-D and HC groups. Welch’s test. C: PCoA of fecal microbiota composition in MCI group and control group based on bray-curtis distance. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. D-F: Relative taxonomic abundances at the level of phylum, family and genus are shown in bar chart.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7011889/v1/7d143d443b88acc891f1a500.jpg"},{"id":87665153,"identity":"cb017d71-2f98-4e28-aeb4-1ee71d9a5078","added_by":"auto","created_at":"2025-07-27 11:02:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98349,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of taxonomic abundances in MCI and control groups. A-B: Welch's test to compare the gut microbia at the family and genus levels between the MCI group and the normal group. C: LEfSe analysis of differential species abundance.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7011889/v1/2aa7621d3787f2e533bbb5ef.jpg"},{"id":87666079,"identity":"389aeb4a-c824-47e0-a7e7-ae5b48b0990e","added_by":"auto","created_at":"2025-07-27 11:10:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163255,"visible":true,"origin":"","legend":"\u003cp\u003eThe relation of diet and MoCA score. A: Heatmap of Spearman’s correlation bewteen the 15 food groups and MocA score in 91 subjects. B: Heatmap of Spearman’s correlation bewteen the 117 food items and MocA score in 91 subjects. C: Heatmap of Spearman’s correlation bewteen the differental food items of two groups and MocA score in 91 subjects. D: Heatmap of Spearman’s correlation bewteen the the differental food items of two groups and MocA score in MCI subjects. *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7011889/v1/3912b6a979dea7d188e70ba9.jpg"},{"id":87666081,"identity":"56130e4b-f6bf-4249-bac4-84481c4baf2f","added_by":"auto","created_at":"2025-07-27 11:10:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":174539,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between gut microbiota and diet or MoCA scores.A: Heatmap of Spearman’s correlation bewteen bean curd\u0026amp;tofu pudding, yogurt, freshwater fish, shrim\u0026amp;crab or pumpkin and abundances of genera in 91 subjects. B: Heatmap of Spearman’s correlation bewteen abundances of genera and MocA scores, age, BMI in 91 subjects. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7011889/v1/b700d9d1ea175c1980d08e04.jpg"},{"id":87665162,"identity":"ec3f2399-f102-47ed-8eb1-4f9f4406e954","added_by":"auto","created_at":"2025-07-27 11:02:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":113208,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between TC or LDL-C and MoCA scores or gut microbiota.A-B: Spearman’s correlation bewteen TC or LDL-C and MoCA scores in 91 subjects. C:Heatmap of Spearman’s correlation bewteen TC or LDL-C and abundances of genera in 48 MCI subjects. *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7011889/v1/f5a215cb9fc4a9371e8a5067.jpg"},{"id":91617862,"identity":"ccc85d84-3db6-43e2-acb4-b7c0ed107d7a","added_by":"auto","created_at":"2025-09-18 10:54:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1513884,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7011889/v1/92d4f759-f392-495a-b32d-136447cdefb3.pdf"},{"id":87665152,"identity":"bb50db80-5da0-476b-874b-79ff7b1c8780","added_by":"auto","created_at":"2025-07-27 11:02:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49794,"visible":true,"origin":"","legend":"","description":"","filename":"tableS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7011889/v1/697d55343e02753d0afd06dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gut Microbiota Mediates Dietary Modulation of Mild Cognitive Impairment in the Elderly: A Cross-Sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eMild cognitive impairment (MCI) refers to a decline in cognitive function that is not consistent with the individual's age or education level, potentially representing an early stage of Alzheimer's disease (AD)[1]. With the global prevalence of AD demonstrating a persistent upward trajectory[2], this neurocognitive condition poses growing societal and healthcare challenges. Early identification and intervention of MCI may prevent or delay the onset of dementia[3], However, the underlying mechanisms leading to MCI remain poorly understood. Therefore,\u0026nbsp;it is crucial to identify controllable factors that may contribute to the development of MCI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmerging evidence substantiates significant associations between nutritional factors and MCI[2, 4, 5], with particular emphasis on differential impacts of different foods and dietary patterns on cognitive function[6, 7].\u0026nbsp;It has been proven that cognitive function is primarily influenced by long-term dietary habits[4, 7], and comprehensive lifestyle changes including diet may significantly improve cognition function after 20 weeks[8]. Several studies have demonstrated that control subjects exhibited significantly higher consumption of fish and eggs compared to MCI subjects[9-11], and higher intake of fish and eggs might reduce risk for cognitive decline[10, 11]. Besides, plant protein intake was correlated with better cognitive score and and a diet rich in nuts, vegetables, and fruits might be shown to improve cognitive ability in adults[7, 12].\u0026nbsp;Accumulating evidence indicates that synergistic dietary patterns integrating foods and nutrients demonstrate superior neuroprotective effects compared to isolated nutritional components, with the Mediterranean diet (MeD) representing the most extensively investigated paradigm[2, 4, 6]. The (MeD) is a dietary pattern characterized by abundant intake of plant-based foods including fruits, vegetables, whole grains, legumes, nuts, olive oil\u0026nbsp;and moderate wine consumption. It incorporates daily consumption of fermented dairy products, while emphasizing seafood over other animal proteins[6, 13]. Recent studies have shown that the (MeD) and DASH(Dietary Approaches to Stop Hypertension) are correlated with a low likelihood of cognitive impairment[13-15].\u0026nbsp;However, the consumption of cholesterol-rich foods may result in elevated serum cholesterol levels, and in a specific population, increased dietary or serum cholesterol has been positively correlated with an accelerated decline in cognitive function[16]. Above all, it is suggested that dietary factors are involved in the development of MCI, but the precise mechanisms underlying diet-mediated cognitive modulation remain poorly understood.\u003c/p\u003e\n\u003cp\u003eThere is a growing belief that intricate interactions exist between diet and gut microbiota, with the composition of the microbiome being principally determined by dietary factors[17, 18]. It was proved that long-time diet could regulation of gut microbes[19], and was closely associated with the anti-inflammatory or pro-inflammatory products of gut microbiome[20].\u0026nbsp;Moreover, emerging evidence delineates precise nutrient-microbiota interactions, with prior study demonstrating that daily yogurt consumption induces transient probiotic (\u003cem\u003eS. thermophilus\u003c/em\u003e and \u003cem\u003eB. lactis\u003c/em\u003e) enrichment[21],\u0026nbsp;and intake of oily fish, fruit, nuts, vegetables legumes and cereals has been linked to an increased abundance of \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e or \u003cem\u003eRoseburia hominis\u003c/em\u003e[20]. The intestinal microbes may utilize ingested nutrients for survival, and microbial metabolic activities significantly influence host physiology[5, 17].\u0026nbsp;Furthermore, recent studies have shown that the gut microbiota has a definite association with cognitive decline[2, 5], and \u003cem\u003eBacteroides\u0026nbsp;\u003c/em\u003ewas found to be independently increased in patients with MCI[22]. In addition,\u0026nbsp;recent studies have revealed that features of gut microbiota and very low-density lipoprotein (VLDL) are associated with a cluster of diseases, including coronary artery disease and cognitive impairment[23]. Notably, animal study demonstrated that \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e effectively ameliorates high-fat, high-cholesterol (HFHC) diet-induced cognitive deficits[24]. Therefore, we presumed that gut microbiota might serve as a critical mediator bridging dietary exposures and MCI pathogenesis.\u003c/p\u003e\n\u003cp\u003eIn this study, we used food frequency questionnaire (FFQ) to reflect the habit of long-term diet[19], and the Montreal Cognitive Assessment (MoCA) to measure the cognitive function. Additionally, we examined the roles of gut microbiota and to clarify the underlying mechanism that connects the diet with cognitive function.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study investigated diet-microbiota-cognition interactions in elderly inpatients at Qilu Hospital. We planned to enroll 100 participants (50 with MCI and 50 age-matched HC). Eligible inpatients aged 55-70 years were recruited between September 2020 and December 2021. Comprehensive clinical data were collected, including gender, age, education level, lipid profiles, and hemoglobin levels. Dietary habits were assessed using a FFQ, and cognitive function was evaluated using the MoCA. Fecal samples were collected for 16S rRNA gene sequencing. Spearman’s correlation analysis assessed correlations between gut microbiota abundance and dietary intake, cognitive scores, and low-density lipoprotein cholesterol (LDL-C).\u003c/p\u003e\n\u003cp\u003eStrict exclusion criteria applied: 1. No history of chronic diseases, such as ischemic heart disease, diabetes mellitus, liver disease, kidney disease, malignant tumor, or cerebral stroke; 2. No history of alcohol abuse; 3. No history of related drug use: no antibiotic use in the last 2 months, no use of drugs that may affect lipid metabolism or antioxidant supplementation, or supplements containing a large number of fatty acids, no use of antidepressants or drugs that act on the central nervous system; 4. Subjects with AD, Parkinson’s disease (PD), or those not completing the food frequency questionnaire or cognitive tests were also excluded from the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA FFQ was used to collect dietary information covering the past one to two years by mobile phone. This questionnaire was adapted from Nutrition and Health Surveillance of the Chinese Population in 2010 (CNHS2010-F). The FFQ comprised 15 categories (rice and whole grains, legumes and legume products, snacks, milk and dairy products, eggs, alcohol, meats and poultry, fish, fruits, nuts and seeds, fungi and seaweeds, vegetables, starch and tuber crop products, pickles, tea, and beverages), which included a total of 117 items (see table S). Each participant was questioned by a trained nutritionist regarding the frequency (per month, week, or day) and quantity of each item, which were subsequently used to estimate the average daily intake of each food.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCognitive\u0026nbsp;assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cognitive function of each participant was assessed by two professional neurologists using the MoCA (Beijing version, www.mocatest.org)[25]. In developed countries, the commonly used cut-off score for screening MCI is 25/26. However, a previous study among Chinese elderly individuals indicated that the cut-off scores varied based on educational level: 13/14 for those with no formal education, 18/19 for those with 1–6 years of education, and 24/25 for those with 7 or more years of education[9]. These adjusted criteria for MCI have been shown to exhibit high sensitivity and specificity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFecal microbiome analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, the morning fecal of each participant was collected in two sterile containers and brought to the laboratory within two hour. All the fecal samples were preserved at−80 °C until processing. The 16s rRNA sequencing was performed by Majorbio (Shanghai, China). Dereplication, discard of singletons (no less than 5 reads in at least 3 samples) and rarefaction based on the minimal number of reads among samples were conducted before analysis. Ace index and Chao index were calculated to evaluate microbial richness in each sample, and Shannon index, Simpson index for alpha-diversity. Principal Coordinates Analysis (PCoA) at the level of operational taxonomy unit (OTU) was used to reveal the dissimilarities of gut microbiota between faecal samples from control subjects and MCI subjects Hierarchical clustering and PCoA on a level of all OTUs were performed based on bray-curtis distances. ANOSIM based on Bray-Curtis distance and 999 times of permutation tests was utilised to analyse structural difference between MCI and HC groups via R. LEfSe calculation was conducted from phylum to genus level between two groups at the LDA score threshold of 2.5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the C programming language to analyze FFQ data using formulas, calculating the daily intake of each food. The clinical and FFQ data from two groups were compared using SPSS22.0. The normality of the measurement data was verified with the Kolmogorov-Smirnov test and expressed as mean ± standard deviation (SD). An independent samples t-test was employed for comparisons between groups; for measurement data with a skewed distribution, expressed as median (Q1, Q3), the Mann-Whitney U test was used for inter-group comparisons. Additionally, the chi-square test was applied to analyze binary categorical variables between the normal and MCI groups. A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003eWe utilized Spearman correlation analysis to investigate the relationships between various dietary components and MoCA scores, between different dietary components and gut microbiota diversity, between gut microbiota diversity and MoCA scores, and between blood lipid levels and MoCA scores across all participants. Additionally, we conducted partial correlation analysis, treating MoCA scores as a covariate, to examine the relationship between diet and levels of total cholesterol (TC) or LDL-C. In the subgroup with MCI, we employed Spearman correlation analysis to explore the association between LDL-C and gut microbiota composition.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic Characteristics and clinical parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 91 Chinese adults (43 HC and 48 MCI subjects) were recruited in the present study\u0026nbsp;(7 HC and 2 MCI subjects were removed from the group due to unqualified stool). This cohort (n=91) is statistically adequate for detecting clinically relevant microbiota-cognition relationships while enabling deep mechanistic exploration. The demographic characteristics of the participants are listed in Table 1. The MCI subjects had a lower education level and got a lower scores of MoCA than the control subjects (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). And participants in control group had a lower lever of total cholesterol and low density lipoprotein cholesterol than the MCI subjects(\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). Moreover, we did not detect any differences in age, gender and orther clinical parameters between control and MCI subjects (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFood intake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, the daily intake of 15 food groups had no statistical differences between control subjects and MCI subjects. Despite no statistical significance, the daily intake of milk and dairy products or fish and marine lives in the control subjects was higher than that in MCI subjects. Besides in the control subjects, the daily intake of bean curd and tofu pudding, other dried beans and products, yogurt, freshwater fish, shrim and crab, pine nut, pumpkin and cucumber preserved with soy paste was higher than that in MCI groups (\u003cem\u003eP \u0026lt;\u0026nbsp;\u003c/em\u003e0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe gut microbiome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStool samples were collected from 43 control subjects and 48 MCI subjects. To determine the diferences in overall gut microbiota diversity between two groups, the alpha and beta diversities were evaluated. Ace and Chao indices were used to characterize bacterial abundance within the groups. These indices revealed had a signifcant difference between the two groups (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.025, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.034, respectively, Fig.1A, B). The beta diversity based on PCoA analysis did not show dramatically different clustering in two groups (\u003cem\u003eP\u003c/em\u003e = 0.46, Fig.1C)\u003c/p\u003e\n\u003cp\u003eUsing sequencing analysis, the composition of the gut microbiota was determined (Fig.1D-F). Among the seven most common bacterial communities, Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria were the main five phyla present in the gut microbiota of the two groups. Although there were no significant differences between the two groups at phylum level, there was a trend of increasing the relative abundance of Bacteroidetes in the MCI groups compared to the normal group (P=0.095) (Fig.1D). The relative abundance of Firmicutes to Bacteroidetes had no difference in two group. At family level the abundance of Oscillospiraceae, norank_o__RF39, norank_o__Elsterales, Bryobacteraceae increased in MCI subjects while Actinomycetaceae decreased (\u003cem\u003eP \u0026lt;\u0026nbsp;\u003c/em\u003e0.05, Fig. 1D, 2A). Besides, comparison at the genus level, \u003cem\u003eUCG-002, norank_f__norank_o__RF39, UCG-003, Sutterella, F0332, Phocea, norank_f__norank_o__Elsterales\u003c/em\u003e and \u003cem\u003eBryobacter\u003c/em\u003e increased in MCI group and \u003cem\u003eBradyrhizobium\u003c/em\u003e only was found in MCI group, but \u003cem\u003eActinomyces\u003c/em\u003e and \u003cem\u003eAtopobium\u0026nbsp;\u003c/em\u003eincreased in control group (\u003cem\u003eP \u0026lt;\u0026nbsp;\u003c/em\u003e0.05, Fig.1F, 2B). LEfSe analysis was performed to measure diferences in taxa in gut microbiota in MCI and normal groups (Fig.2C). LDA comparison at the genus level showed that MCI group had increased \u003cem\u003eCoprococcus\u003c/em\u003e, \u003cem\u003eDesulfovibrio\u003c/em\u003e, \u003cem\u003eSutterella\u003c/em\u003e, \u003cem\u003eUCG-003\u003c/em\u003e and \u003cem\u003enorank_f__norank_o__RF39\u0026nbsp;\u003c/em\u003ebut decreased \u003cem\u003eEubacterium\u003c/em\u003e_\u003cem\u003eeligens\u003c/em\u003e_group, \u003cem\u003eRuminococcus\u003c/em\u003e_\u003cem\u003egnavus\u003c/em\u003e_group and \u003cem\u003eStreptococcus\u003c/em\u003e compared to the HC group (\u003cem\u003eP \u0026lt;\u0026nbsp;\u003c/em\u003e0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between Diet and Cognitive Function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in figure 3, in the 15 food groups, spearman\u0026rsquo;s correlation analysis showed that the daily intake of milk and dairy products or fish and marine lives by all subjects was positively correlated with the MoCA score (r = 0.249 or 0.281, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; n = 91; Fig.3A); in the 117 food items, the daily intake of cream was positively associated with the MoCA score(r = 0.210, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; n = 91; Fig.3B,3C), as was freshwater fish, pine nut or preserved szechuan pickle(r = 0.213, 0.269 or 0.213, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; n = 91; Fig.3B,3C), moreover, the daily intake of yogurt or shrim and crab had a significant positive correlation with the MoCA score (r = 0.396 or 0.294, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; n = 91; Fig.3B,C),but other intake foods with differences between the two groups only had a trend to positive correlation with the MoCA score(Fig.3B,C). In the MCI group, the daily intake of cooked rice was positively associated with the MoCA score (r = 0.317, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05; n = 48; Fig.3D), so was cooked rice with other grains, oatmeal, freshwater fish, orange, coriander, cabbage, tomato, carrot or preserved szechuan pickle(r = 0.285, 0.308, 0.287, 0.368, 0.415, 0.345, 0.432, 0.360 or 0.336, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; n = 48; Fig.3D), and the daily intake of yogurt or shrim and crab had a trend to positive correlation with the MoCA score(r = 0.248 or 0.281, \u003cem\u003eP\u003c/em\u003e = 0.090 or 0.053; n = 48; Fig.3D), but the daily intake of chicken or Chinese cabbage was negatively associated with the MoCA score (r = -0.387 or -0.319, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; n = 48; Fig.3D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe gut microbiome correlated with the diet and MoCA score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined associations between compositions of gut microbiota and those significantly different diet using Spearman\u0026rsquo;s correlation coefficient(Fig.4A). We observed the differential bacteria in the two groups, Significant correlations were found between relative abundances of genus UCG-002 and pumpkin(r = -0.255, \u003cem\u003eP\u003c/em\u003e = 0.0147), UCG-003 and pumpkin(r = -0.226, \u003cem\u003eP\u003c/em\u003e = 0.0311), UCG-003 and Shrim\u0026amp;crab(r= -0.189, \u003cem\u003eP\u003c/em\u003e = 0.072), \u003cem\u003eActinomyces\u003c/em\u003e and freshwater fish (r = 0.221, \u003cem\u003eP\u003c/em\u003e = 0.0353), \u003cem\u003eCoprococcus\u003c/em\u003e and pumpkin (r = -0.246, \u003cem\u003eP\u003c/em\u003e = 0.019), \u003cem\u003eDesulfovibrio\u003c/em\u003e and Shrim\u0026amp;crab (r = -0.217, \u003cem\u003eP\u003c/em\u003e = 0.0384), \u003cem\u003eDesulfovibrio\u003c/em\u003e and yogurt (r = -0.184, \u003cem\u003eP\u003c/em\u003e = 0.080), \u003cem\u003eDesulfovibrio\u003c/em\u003e and pumpkin (r = -0.181, \u003cem\u003eP\u003c/em\u003e = 0.086), \u003cem\u003eRuminococcus_gnavus_group\u003c/em\u003e and pumpkin (r = 0.279, \u003cem\u003eP\u003c/em\u003e = 0.0073), \u003cem\u003eRuminococcus_gnavus_group\u003c/em\u003e and yogurt(r = -0.244, \u003cem\u003eP\u003c/em\u003e = 0.0199), \u003cem\u003eStreptococcus\u003c/em\u003e and yogurt(r = 0.179, \u003cem\u003eP\u003c/em\u003e = 0.0894). Beside, \u003cem\u003enorank_f__norank_o__RF39\u003c/em\u003e, \u003cem\u003eSutterella Eubacterium_eligens_group\u003c/em\u003e had no significant correlation with diet.\u003c/p\u003e\n\u003cp\u003eFourtherly, Spearman\u0026rsquo;s correlation analysis showed that relative abundances of genus \u003cem\u003eActinomyces\u003c/em\u003e was positively correlated with the MoCA score (r = 0.287, \u003cem\u003eP\u003c/em\u003e = 0.0058), so as was \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eEubacterium_hallii_group\u003c/em\u003e or \u003cem\u003eBlautia\u0026nbsp;\u003c/em\u003e(r = 0.254 \u003cem\u003eP\u003c/em\u003e = 0.0151, r = 0.286, \u003cem\u003eP\u003c/em\u003e = 0.006, r = 0.215 \u003cem\u003eP\u003c/em\u003e = 0.041). \u0026nbsp;In contrast, the negative correlation was found between relative abundances of genus \u003cem\u003eNegativibacillus\u003c/em\u003e with the MoCA score (r = -0.233, \u003cem\u003eP\u003c/em\u003e = 0.0261), \u003cem\u003eSutterella\u003c/em\u003e with the MoCA score (r = -0.232, \u003cem\u003eP\u003c/em\u003e = 0.0268), UCG-003 with the MoCA score (r = -0.359, \u003cem\u003eP\u003c/em\u003e = 0.0004), \u003cem\u003enorank_f__norank_o__RF39\u003c/em\u003e with the MoCA score (r = -0.232, \u003cem\u003eP\u003c/em\u003e = 0.0266), \u003cem\u003eDesulfovibrio\u003c/em\u003e with the MoCA score (r = -0.241, \u003cem\u003eP\u003c/em\u003e = 0.0212), \u003cem\u003eAlloprevotella\u003c/em\u003e with the MoCA score (r = -0.214, \u003cem\u003eP\u003c/em\u003e = 0.0413), NK4A214_group with the MoCA score (r = -0.220, \u003cem\u003eP\u003c/em\u003e = 0.0365), \u003cem\u003eCoprococcus\u003c/em\u003e with the MoCA score (r = -0.189, \u003cem\u003eP\u003c/em\u003e = 0.0732).Besides, \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eSutterella\u003c/em\u003e, UCG-003, \u003cem\u003enorank_f__norank_o__RF39\u003c/em\u003e or \u003cem\u003eDesulfovibrio\u003c/em\u003e had no significant correlation with age or BMI (Fig.4B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe TC or LDL-C correlated with the diet, MoCA score and the gut microbiome.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing a partial correlation to Analyze the association between significantly different diet with the level of TC or LDL-C in the serum, we obtained a negative correlation between yogurt and the level of TC(r = -0.257, \u003cem\u003eP\u003c/em\u003e = 0.012) or LDL-C(r = -0.262, \u003cem\u003eP\u003c/em\u003e = 0.013) in serum by treating MocA as a control variable. Using spearman\u0026rsquo;s correlation analysis，we got that the level of TC or LDL-C in the serum is negatively with MoCA score(r = -0.1339, \u003cem\u003eP\u003c/em\u003e = 0.2162 Fig.5A; r = -0.2502, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.0194 Fig.5B). Beisdes, using the spearman correlation analysis, we found that there may be a positive correlation between the level of LDL-C in the serum with the relative abundances of genus UCG-003 or \u003cem\u003eDesulfovibrio\u003c/em\u003e or \u003cem\u003eCoprococcus\u003c/em\u003e or \u003cem\u003enorank_f__norank_o__RF39\u003c/em\u003e(r = 0.137 or 0.0794 or 0.127 or 0.470, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05, Fig.5C), and a negative correlation between the level of LDL-C in the serum with the relative abundances of genus \u003cem\u003eActinomyces\u003c/em\u003e or \u003cem\u003eStreptococcus\u003c/em\u003e(r = -0.143 or -0.124, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05, Fig.5C) in MCI group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Demographic Characteristics and clinical parameters\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC (n=43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCI (n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e57.07\u0026plusmn;6.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e59.17\u0026plusmn;5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eGender, male(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e67.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e56.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e11.92\u0026plusmn;3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8.55\u0026plusmn;3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e25.01\u0026plusmn;2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e25.24\u0026plusmn;2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMoCA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e24.5(23.5,25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e19.5(17,21.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e4.54\u0026plusmn;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e4.98\u0026plusmn;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.039*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.37\u0026plusmn;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.44\u0026plusmn;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.3\u0026plusmn;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.34\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2.48\u0026plusmn;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2.85\u0026plusmn;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003esdLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.71\u0026plusmn;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.82\u0026plusmn;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFatty acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e48.72\u0026plusmn;19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e48.27\u0026plusmn;16.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e142.55\u0026plusmn;13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e142.32\u0026plusmn;14.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI: body mass index; TC: total cholesterol; TG: triglyceride; HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol; sdLDL-C: small, dense low-density lipoprotein; Hb: hemoglobin. * \u003cem\u003eP \u0026lt;\u0026nbsp;\u003c/em\u003e0.05 was considered to be statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e Daily intake of detailed food items in control and MCI groups.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFood categorie\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC (g/d)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCI (g/d)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003erice and whole grain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e407.29(285.12,570.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e403.21(284.36,489,48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eLegume and legume product\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e100.00(52.67,159.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e84.11(45.95,148.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eBean curd\u0026amp;tofu pudding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e14.29(8.57,28.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e10.00(2.08,21.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.035*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eOther dried beans and products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e0(0,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0(0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.040*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003esnacks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e11.07(1.90,28.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e9.14(1.25,36.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003emilk and dairy products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e250.00(75.00,295.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e236.61(35.77,311.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eyogurt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e21.42(0,53.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0(0,22.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eeggs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e84.67(62.33,120.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e120.00(76.75,132.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003ealcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e21.43(0.00,105.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e33.66(0.00,142.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003emeats and poultry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e111.67(41.91,217.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e83.69(37.77,131.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003efish and marine lives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e37.62(16.67,61.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e21.20(11.81,40.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003efreshwater fish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e8.33(3.33,28.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.5(0,14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.036*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eShrim\u0026amp;crab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e2.85(0,8.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.41(0,3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.049*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003efruits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e187.62(80.95,292.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e210.71(71.55,300.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eNuts and seeds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e28.76(10.31,40.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e17.95(8.26,36.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003epine nut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e0(0,0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0(0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eFungi and seaweeds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e30.00(11.19,61.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e27.50(13.40,62.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003evegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e316.43(228.10,439.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e323.21(237.89,426.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eChinese watermelon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e6.67(0,14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3.33(0,6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003epumpkin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e14.29(3.33,28.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.16(0,14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.032*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003estarch and tuber crops product\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e5.00(1.67,14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e6.55(1.49,13.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003epickles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e0.64(0,5.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.78(0.00,10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eCucumber preserved with soy paste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e0(0,2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0(0,0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.043*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003etea and beverages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e255.58(10.00,1001.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e73.12(5.63,517.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings reveal significant associations between cognitive preservation and consumption of dairy products (notably yogurt) and aquatic foods (freshwater fish and crustaceans), with pine nuts also exhibiting neuroprotective effects. Genus-level gut \u0026nbsp;microbiota analysis revealed distinct compositional differences between MCI and control groups. Notably, \u003cem\u003eActinomyces\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Streptococcus\u003c/em\u003e were identified as potential mediators of dietary neuroprotection, whereas UCG-003 and \u003cem\u003eDesulfovibrio\u003c/em\u003e appeared to counteract these beneficial effects. Furthermore, serum LDL-C levels exhibited dual negative correlations with both yogurt consumption and cognitive performance, suggesting a potential link with the observed microbiota alterations. Collectively, these findings support a novel mechanistic pathway linking dietary patterns to MCI risk through gut microbiota-mediated serum LDL-C modulation, underscoring the critical role of the gut-brain axis in \u0026nbsp;dietary influences on cognitive preservation.\u003c/p\u003e\n\u003cp\u003eIn this study, we observed that participants in the normal group tended to consume more milk and dairy products, as well as fish and marine lives, compared to those in the MCI group. Additionally, among 117 surveyed food items, the control group showed preferential intake of legumes ( bean curd and tofu pudding, other dried beans and products), yogurt,\u0026nbsp;freshwater fish,\u0026nbsp;crustaceans (shrimp and crab), pine nuts and pumpkin (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) - dietary patterns aligning with established neuroprotective nutritional profiles reported in prior studies[6, 12, 26]. Existing cohort studies reveal that older adults (\u0026ge;65 years) consuming one or more servings of fish per week exhibit 0.35% slower cognitive decline rates than those with lower intake[11]. Furthermore, elevated consumption of crustaceans (shrimp, scallop and lobster) and legumes (peas and lima beans) correlated with a reduced risk of subjective cognitive decline[7]. Recent evidence further identifies legumes, liquid-type yogurt and curd-type yogurt intake as significant protective factors against MCI[27]. Mechanistically, pumpkin is regarded as beneficial for brain health due to its rich content of flavonoids, phenolic compounds, vitamins, and carbohydrates[28], with animal models confirming cognitive preservation via traditional Mesoamerican diets combining pumpkin, fish, and black beans and other nutritious components[29]. Our analysis of 91 participants revealed significant positive associations between MoCA scores and consumption of two key food categories: milk and dairy products or fish and marine lives. Notably, cream, yogurt, freshwater fish, shrimp and crab or pine nut was significantly positively correlated with the cognitive function only pumpkin has a trend, indicating that certain foods might have an advantageous impact on cognitive function. Additionally, the combination of these varied foods between two groups\u0026nbsp;is similar to the Mediterranean diet, which prevented the decline of cognitive function by the nutrition from fish, dairy, fruit and so on[30]. Thus, the effects of these varied foods on mild cognitive impairment may be attributed to improved nutrient intake, warranting further exploration.\u003c/p\u003e\n\u003cp\u003eAn increasing number of population-based studies indicate that changes in gut microbiota are closely associated with the occurrence of mild cognitive impairment[31, 32]. Previous studies have shown that abundance of phyla Bacteroidetes are significantly increased in the gut microbiota of patients with MCI and Alzheimer\u0026apos;s disease[22, 32], but our cohort showed only abundance of phyla Bacteroidetes elevation (\u003cem\u003eP\u003c/em\u003e=0.095) without statistical significance versus control. In this study, comparative analysis revealed significantly higher abundances of the Actinomycetaceae family and\u003cem\u003e\u0026nbsp;Actinomyces\u003c/em\u003e genus in the normal group compared to the MCI group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). This finding aligns with previous reports demonstrating reduced Actinobacteria phylum abundance in Alzheimer\u0026apos;s disease patients[33]. Notably, emerging evidence suggests a neuroprotective association, as Actinomycetaceae levels show significantly positive correlation with total MoCA scores in peritoneal dialysis patients[31]. Besides, we found that \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eEubacterium_eligens_group\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Ruminococcus_gnavus_group\u003c/em\u003e genus were increased in normal group compared with MCI group. Prior clinical observations by Wang and colleagues demonstrated similar \u003cem\u003eStreptococcus\u0026nbsp;\u003c/em\u003ewhich was positively correlated with MMSE scores enrichment in cognitively intact peritoneal dialysis patients, and Streptococcaceae and Atopobium showing positive correlations with both MMSE and MoCA scores independent of confounding age factors[31]. Moreover, animal experiments indicated that cognitive function improved in aged mice fed with blueberry-mulberry extract (BME), and the relative abundance of\u003cem\u003e\u0026nbsp;Streptococcus\u003c/em\u003e in the gut increased[34]. However, at the genus level, the MCI group exhibited marked microbial dysbiosis characterized by significant enrichment of sulfate-reducing taxa (\u003cem\u003eDesulfovibrio\u003c/em\u003e), pro-inflammatory commensals (\u003cem\u003eSutterella\u003c/em\u003e), and multiple unclassified genera (UCG-002, UCG-003,etc.) compared to cognitively normal controls (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Critically, this dysbiotic pattern mirrors Alzheimer\u0026apos;s-associated microbiota alterations, where Desulfovibrionales order and Desulfovibrionaceae family overabundance exhibits causal associations with AD pathogenesis[35]. While\u0026nbsp;the UCG-003 genus of the Oscillospiraceae family remains understudied in AD or MCI, it has been found to be enriched in patients with acute ischemic stroke exhibiting mild to moderate neurological deficits[36]. Notably, subjective memory complaints exhibit dose-dependent relationships with Sutterella (+25% per complaint unit) and Desulfovibrionaceae (+27%)[37]. Collectively, our findings implicate \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, UCG-003, \u003cem\u003eSutterella\u003c/em\u003e, and \u003cem\u003eDesulfovibrio\u003c/em\u003e as potential mediators of MCI pathogenesis. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDietary modulation of gut microbial ecology directly influences host body, which might result in beneficial or detrimental consequences on host health[17, 18]. Our Spearman analysis revealed \u003cem\u003eActinomyces\u003c/em\u003e abundance as positively correlated with both freshwater fish intake and cognitive scores. Furthermore, yogurt consumption trended toward positive association with \u003cem\u003eStreptococcus\u003c/em\u003e, which itself demonstrated significant correlation with cognitive levels. Emerging evidence suggests dietary modulation of cognition may occur through microbiota-dependent pathways.While Actinobacteria are abundant in freshwater ecosystems and fish microbiota[38], human gut Actinobacteria responses to fish consumption remain unexplored. Beisdes, Actinobacteria depletion manifests in AD rodent models[39] and cognitively impaired peritoneal dialysis patients, where \u003cem\u003eActinomyces\u003c/em\u003e abundance positively correlates with cognitive scores[31]. Similarly, yogurt consumption elevates intestinal \u003cem\u003eStreptococcus\u003c/em\u003e, particularly neuroprotective S. thermophilus[40]\u003csup\u003e,\u003c/sup\u003e[41]. This is corroborated by Danggui Shaoyao San improving cognition in AD rats via \u003cem\u003eStreptococcus\u003c/em\u003e enrichment[39], and S. thermophilus MN-ZLW-002 mitigating cognitive deficits in APP/PS1 mice through gut-brain axis modulation[42]. Collectively, \u003cem\u003eActinomyces\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Streptococcus\u003c/em\u003e may emerge as key mediators of freshwater fish and yogurt neuroprotection. However, we also discovered that both pumpkin and crustacean were inversely correlated with Oscillospiraceae_UCG-003, which was significantly negatively correlated with cognitive scores. Similarly, \u003cem\u003eDesulfovibrio\u003c/em\u003e abundance showed inverse relationships not only with crustaceans, yogurt, pumpkin but also with cognitive levels. Animal experiments found that a high-fat diet which caused cognitive dysfunction[43] altered the composition of the gut microbiota, significantly upregulated lead to Oscillospiraceae_UCG-003[44] and Desulfovibrio[45, 46]. Besides, AD models exhibit \u003cem\u003eDesulfovibrio\u003c/em\u003e enrichment versus controls[39] and a water-soluble polysaccharide extracted from mussels reversed the increase of \u003cem\u003eDesulfovibrio\u003c/em\u003e in immunosuppressed mice[47]. Collectively, we believe that UCG-003 and \u003cem\u003eDesulfovibrio\u003c/em\u003e likely antagonize the cognitive benefits of pumpkin, shrimp-crab or yogurt.\u003c/p\u003e\n\u003cp\u003ePrevious studies found that serum TC and LDL-C levels were elevated in AD and MCI, playing a significant role in the pathophysiological processes of both conditions[48, 49]. In addtion, yogurt consumption reduced these atherogenic lipids and might ameliorate cognitive decline[50]. It has proved that Desulfovibrio is positively correlated with serum TC and LDL-C[51], and probiotic yogurt probiotic yogurt suppresses this LPS-producing genus while reducing the level of serum TG, TC, and LDL-C in HFD-fed hamsters[52]. Network meta-analysis identified the combination of \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e as optimal for lipid-lowering efficacy[53]. corroborated by \u003cem\u003eStreptococcus\u003c/em\u003e \u003cem\u003ethermophilus\u003c/em\u003e fermented milk significantly lowering LDL-C in aadults with subclinical hypercholesterolemia[54]. Our analysis revealed elevated serum TC and LDL-C levels in MCI patients versus normal controls. Partial correlation analysis demonstrated significant inverse associations between yogurt consumption and both TC and LDL-C. Critically, LDL-C levels negatively correlated with MoCA scores. In MCI patients, the serum LDL-C level might be positively correlated with genus \u003cem\u003eDesulfovibrio\u003c/em\u003e, but negatively correlated with genus\u003cem\u003e\u0026nbsp;Streptococcus\u003c/em\u003e. In summary, we conclused that consuming yogurt may affect the levels of \u003cem\u003eDesulfovibrio\u003c/em\u003e or \u003cem\u003eStreptococcus\u003c/em\u003e in gut, which may reduce the serum LDL-C level, resulting in improving the cognitive function.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the small sample size is a drawback. Furthermore, we only use Spearman\u0026rsquo;s correlation to analyse the relation among diet, gut microbiota and cognitive function, so there may be some potentially uncontrolled covariates during data analysis. Therefore, a large-scale cohort study is needed to clarify the relationship between diet and cognitive performance. Moreover, the participants in this study were mainly Chinese, so our findings maybe not necessarily applicable to other populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur finding reveals that the consumption of yogurt, freshwater fish, crustaceans (shrimp and crab), pine nuts, and pumpkin exerts a protective effect on cognitive function. Furthermore, \u003cem\u003eActinomyces\u003c/em\u003e or \u003cem\u003eStreptococcus\u003c/em\u003e may mediate the beneficial effect of freshwater fish or yogurt on cognition, while UCG-003 or \u003cem\u003eDesulfovibrio\u003c/em\u003e might hinder the protective effect of crustaceans or yogurt on cognition. Particularly, yogurt might decrease \u003cem\u003eDesulfovibrio\u003c/em\u003e and increase\u003cem\u003e\u0026nbsp;Streptococcus\u003c/em\u003e, subsequently reducing LDL levels to enhance cognitive function via the microbiota-lipid-brain axis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eMild cognitive impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eHealthy controls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eFFQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eFood frequency questionnaire\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eMoCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eMontreal Cognitive Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eLow-density lipoprotein cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eAlzheimer\u0026apos;s disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eMeD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eMediterranean diet\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eDASH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eDietary Approaches to Stop Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eVLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eVery low-density lipoprotein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eParkinson\u0026rsquo;s disease\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eOUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eOperational taxonomy unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 480px;\"\u003e\n \u003cp\u003eTotal cholesterol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe procedures followed the ethical standards of the Helsinki Declaration of 1975. Written informed consent was obtained from all participants. The study protocol was approved by the Human Ethics Committee of Qilu Hospital (No. KYLL-202008-184).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Shandong Province Medical and Health Science and Technology Development Program (2018WS304 and 202103030834).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuarantor of the article: X-LZ. Specific Author contributions: H-YZ and X-LZ designed and planned the study. H-YZ and S-CF collected the data. L-XL and L-MZ performed statistical analysis. H-YZ and\u0026nbsp;X-PL prepared the original draft. H-YZ and F-XC wrote the manuscript. L-XL,X-LZ and Y-QL supported editing of the original draft and critically revised the manuscript. All authors approved the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all patients in this study and the help of the Department of Psychology at Qilu Hospital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC et al: The diagnosis of mild cognitive impairment due to Alzheimer\u0026apos;s disease: recommendations from the National Institute on Aging-Alzheimer\u0026apos;s Association workgroups on diagnostic guidelines for Alzheimer\u0026apos;s disease. ALZHEIMERS DEMENT 2011, 7(3):270-279.\u003c/li\u003e\n\u003cli\u003eDissanayaka DMS, Jayasena V, Rainey-Smith SR, Martins RN, Fernando WMAD: The Role of Diet and Gut Microbiota in Alzheimer\u0026apos;s Disease. NUTRIENTS 2024, 16(3):412.\u003c/li\u003e\n\u003cli\u003eJongsiriyanyong S, Limpawattana P: Mild Cognitive Impairment in Clinical Practice: A Review Article. AM J ALZHEIMERS DIS 2018, 33(8):500-507.\u003c/li\u003e\n\u003cli\u003eWu J, Song X, Chen GC, Neelakantan N, van Dam RM, Feng L, Yuan JM, Pan A, Koh WP: Dietary pattern in midlife and cognitive impairment in late life: a prospective study in Chinese adults. AM J CLIN NUTR 2019, 110(4):912-920.\u003c/li\u003e\n\u003cli\u003eRomanenko M, Kholin V, Koliada A, Vaiserman A: Nutrition, Gut Microbiota, and Alzheimer\u0026apos;s Disease. FRONT PSYCHIATRY 2021, 12:712673.\u003c/li\u003e\n\u003cli\u003eScarmeas N, Anastasiou CA, Yannakoulia M: Nutrition and prevention of cognitive impairment. LANCET NEUROL 2018, 17(11):1006-1015.\u003c/li\u003e\n\u003cli\u003eYeh TS, Yuan C, Ascherio A, Rosner BA, Blacker D, Willett WC: Long-term dietary protein intake and subjective cognitive decline in US men and women. AM J CLIN NUTR 2022, 115(1):199-210.\u003c/li\u003e\n\u003cli\u003eOrnish D, Madison C, Kivipelto M, Kemp C, McCulloch CE, Galasko D, Artz J, Rentz D, Lin J, Norman K et al: Effects of intensive lifestyle changes on the progression of mild cognitive impairment or early dementia due to Alzheimer\u0026apos;s disease: a randomized, controlled clinical trial. Alzheimer\u0026apos;s research \u0026amp; therapy 2024, 16(1):117-122.\u003c/li\u003e\n\u003cli\u003eYuan L, Zhen J, Ma W, Cai C, Huang X, Xiao R: The Erythrocyte Fatty Acid Profile and Cognitive Function in Old Chinese Adults. NUTRIENTS 2016, 8(7):385.\u003c/li\u003e\n\u003cli\u003eZhao X, Yuan L, Feng L, Xi Y, Yu H, Ma W, Zhang D, Xiao R: Association of dietary intake and lifestyle pattern with mild cognitive impairment in the elderly. J NUTR HEALTH AGING 2015, 19(2):164-168.\u003c/li\u003e\n\u003cli\u003eQin B, Plassman BL, Edwards LJ, Popkin BM, Adair LS, Mendez MA: Fish intake is associated with slower cognitive decline in Chinese older adults. J NUTR 2014, 144(10):1579-1585.\u003c/li\u003e\n\u003cli\u003eDong L, Xiao R, Cai C, Xu Z, Wang S, Pan L, Yuan L: Diet, lifestyle and cognitive function in old Chinese adults. ARCH GERONTOL GERIAT 2016, 63:36-42.\u003c/li\u003e\n\u003cli\u003eVinciguerra F, Graziano M, Hagn\u0026auml;s M, Frittitta L, Tumminia A: Influence of the Mediterranean and Ketogenic Diets on Cognitive Status and Decline: A Narrative Review. NUTRIENTS 2020, 12(4):1019.\u003c/li\u003e\n\u003cli\u003eTsivgoulis G, Judd S, Letter AJ, Alexandrov AV, Howard G, Nahab F, Unverzagt FW, Moy C, Howard VJ, Kissela B et al: Adherence to a Mediterranean diet and risk of incident cognitive impairment. NEUROLOGY 2013, 80(18):1684-1692.\u003c/li\u003e\n\u003cli\u003eDuplantier SC, Gardner CD: A Critical Review of the Study of Neuroprotective Diets to Reduce Cognitive Decline. NUTRIENTS 2021, 13(7):2264.\u003c/li\u003e\n\u003cli\u003eAn Y, Zhang X, Wang Y, Wang Y, Liu W, Wang T, Qin Z, Xiao R: Longitudinal and nonlinear relations of dietary and Serum cholesterol in midlife with cognitive decline: results from EMCOA study. MOL NEURODEGENER 2019, 14(1):51.\u003c/li\u003e\n\u003cli\u003eGentile CL, Weir TL: The gut microbiota at the intersection of diet and human health. SCIENCE 2018, 362(6416):776-780.\u003c/li\u003e\n\u003cli\u003eZmora N, Suez J, Elinav E: You are what you eat: diet, health and the gut microbiota. NAT REV GASTRO HEPAT 2019, 16(1):35-56.\u003c/li\u003e\n\u003cli\u003eWu GD, Chen J, Hoffmann C, Bittinger K, Chen Y, Keilbaugh SA, Bewtra M, Knights D, Walters WA, Knight R et al: Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes. Science (American Association for the Advancement of Science) 2011, 334(6052):105-108.\u003c/li\u003e\n\u003cli\u003eBolte LA, Vich VA, Imhann F, Collij V, Gacesa R, Peters V, Wijmenga C, Kurilshikov A, Campmans-Kuijpers M, Fu J et al: Long-term dietary patterns are associated with pro-inflammatory and anti-inflammatory features of the gut microbiome. GUT 2021, 70(7):1287-1298.\u003c/li\u003e\n\u003cli\u003eLe Roy CI, Kurilshikov A, Leeming ER, Visconti A, Bowyer RCE, Menni C, Falchi M, Koutnikova H, Veiga P, Zhernakova A et al: Yoghurt consumption is associated with changes in the composition of the human gut microbiome and metabolome. BMC MICROBIOL 2022, 22(1).\u003c/li\u003e\n\u003cli\u003eSaji N, Murotani K, Hisada T, Tsuduki T, Sugimoto T, Kimura A, Niida S, Toba K, Sakurai T: The relationship between the gut microbiome and mild cognitive impairment in patients without dementia: a cross-sectional study conducted in Japan. SCI REP-UK 2019, 9(1):19227.\u003c/li\u003e\n\u003cli\u003eXu S, Liu Y, Wang Q, Liu F, Xian Y, Xu F, Liu Y: Gut microbiota in combination with blood metabolites reveals characteristics of the disease cluster of coronary artery disease and cognitive impairment: a Mendelian randomization study. FRONT IMMUNOL 2024, 14.\u003c/li\u003e\n\u003cli\u003eHigarza SG, Arboleya S, Arias JL, Gueimonde M, Arias N: Akkermansia muciniphila and environmental enrichment reverse cognitive impairment associated with high-fat high-cholesterol consumption in rats. GUT MICROBES 2021, 13(1):1-20.\u003c/li\u003e\n\u003cli\u003eLu J, Li D, Li F, Zhou A, Wang F, Zuo X, Jia XF, Song H, Jia J: Montreal cognitive assessment in detecting cognitive impairment in Chinese elderly individuals: a population-based study. J GERIATR PSYCH NEUR 2011, 24(4):184-190.\u003c/li\u003e\n\u003cli\u003eTheodore LE, Kellow NJ, McNeil EA, Close EO, Coad EG, Cardoso BR: Nut Consumption for Cognitive Performance: A Systematic Review. Advances in nutrition (Bethesda, Md.) 2021, 12(3):777-792.\u003c/li\u003e\n\u003cli\u003eKim KY, Yun JM: Association between diets and mild cognitive impairment in adults aged 50 years or older. NUTR RES PRACT 2018, 12(5):415-425.\u003c/li\u003e\n\u003cli\u003eKim NR, Kim HY, Kim MH, Kim HM, Jeong HJ: Improvement of depressive behavior by Sweetme Sweet Pumpkin and its active compound, beta-carotene. LIFE SCI 2016, 147:39-45.\u003c/li\u003e\n\u003cli\u003eAvila-Nava A, Noriega LG, Tovar AR, Granados O, Perez-Cruz C, Pedraza-Chaverri J, Torres N: Food combination based on a pre-hispanic Mexican diet decreases metabolic and cognitive abnormalities and gut microbiota dysbiosis caused by a sucrose-enriched high-fat diet in rats. MOL NUTR FOOD RES 2017, 61(1).\u003c/li\u003e\n\u003cli\u003eRoman GC, Jackson RE, Gadhia R, Roman AN, Reis J: Mediterranean diet: The role of long-chain omega-3 fatty acids in fish; polyphenols in fruits, vegetables, cereals, coffee, tea, cacao and wine; probiotics and vitamins in prevention of stroke, age-related cognitive decline, and Alzheimer disease. REV NEUROL-FRANCE 2019, 175(10):724-741.\u003c/li\u003e\n\u003cli\u003eWang J, Wu S, Zhang J, Li Y, Wu Y, Qi X: Correlation between gut microbiome and cognitive impairment in patients undergoing peritoneal dialysis. BMC NEPHROL 2023, 24(1).\u003c/li\u003e\n\u003cli\u003eSheng C, Yang K, He B, Du W, Cai Y, Han Y: Combination of gut microbiota and plasma amyloid-beta as a potential index for identifying preclinical Alzheimer\u0026apos;s disease: a cross-sectional analysis from the SILCODE study. ALZHEIMERS RES THER 2022, 14(1):35.\u003c/li\u003e\n\u003cli\u003eVogt NM, Kerby RL, Dill-McFarland KA, Harding SJ, Merluzzi AP, Johnson SC, Carlsson CM, Asthana S, Zetterberg H, Blennow K et al: Gut microbiome alterations in Alzheimer\u0026apos;s disease. SCI REP-UK 2017, 7(1):13537.\u003c/li\u003e\n\u003cli\u003eLi H, Xiao C, Wang F, Guo X, Zhou Z, Jiang Y: Blueberry-Mulberry Extract Alleviates Cognitive Impairment, Regulates Gut Metabolites, and Inhibits Inflammation in Aged Mice. FOODS 2023, 12(4).\u003c/li\u003e\n\u003cli\u003eGuo X, Zhang X, Tang P, Chong L, Li R: Integration of genome-wide association studies (GWAS) and microbiome data highlights the impact of sulfate-reducing bacteria on Alzheimer\u0026apos;s disease. AGE AGEING 2023, 52(7).\u003c/li\u003e\n\u003cli\u003eChou PS, Hung WC, Yang IH, Kuo CM, Wu MN, Lin TC, Fong YO, Juan CH, Lai CL: Predicting Adverse Recanalization Therapy Outcomes in Acute Ischemic Stroke Patients Using Characteristic Gut Microbiota. MICROORGANISMS 2023, 11(8).\u003c/li\u003e\n\u003cli\u003eWu F, Davey S, Clendenen TV, Koenig KL, Afanasyeva Y, Zhou B, Bedi S, Li H, Zeleniuch-Jacquotte A, Chen Y: Gut Microbiota and Subjective Memory Complaints in Older Women. J ALZHEIMERS DIS 2022, 88(1):251-262.\u003c/li\u003e\n\u003cli\u003eSadeghi J, Chaganti SR, Johnson TB, Heath DD: Host species and habitat shape fish-associated bacterial communities: phylosymbiosis between fish and their microbiome. MICROBIOME 2023, 11(1):1-258.\u003c/li\u003e\n\u003cli\u003eJin Y, Liang S, Qiu J, Jin J, Zhou Y, Huang Y, He C, Yu W, Deng S, Cheng S et al: Intestinal flora study reveals the mechanism of Danggui Shaoyao San and its decomposed recipes to improve cognitive dysfunction in the rat model of Alzheimer\u0026apos;s disease. FRONT CELL INFECT MI 2023, 13:1323674.\u003c/li\u003e\n\u003cli\u003eKim C, Yoon L, Michels K, Tranfield W, Jacobs J, May F: The Impact of Prebiotic, Probiotic, and Synbiotic Supplements and Yogurt Consumption on the Risk of Colorectal Neoplasia among Adults: A Systematic Review. NUTRIENTS 2022, 14(22):4937.\u003c/li\u003e\n\u003cli\u003eKok CR, Hutkins R: Yogurt and other fermented foods as sources of health-promoting bacteria. NUTR REV 2018, 76(Supplement_1):4-15.\u003c/li\u003e\n\u003cli\u003eZhang Y, Wang Y, Zhou Z, Yang Y, Zhao J, Kang X, Li Z, Shen X, He F, Cheng R: Live and Heat-Inactivated Streptococcus thermophilus MN-ZLW-002 Mediate the Gut-Brain Axis, Alleviating Cognitive Dysfunction in APP/PS1 Mice. NUTRIENTS 2024, 16(6):844.\u003c/li\u003e\n\u003cli\u003eTan BL, Norhaizan ME: Effect of High-Fat Diets on Oxidative Stress, Cellular Inflammatory Response and Cognitive Function. NUTRIENTS 2019, 11(11):2579.\u003c/li\u003e\n\u003cli\u003eSun Y, Liu X, Wang R, Liu R, Lv X, Ma Y, Li Q: Lacticaseibacillus rhamnosus HF01 fermented yogurt alleviated high-fat diet-induced obesity and hepatic steatosisvia the gut microbiota\u0026ndash;butyric acid\u0026ndash;hepatic lipid metabolism axis. FOOD FUNCT 2024, 15(8):4475-4489.\u003c/li\u003e\n\u003cli\u003eZhai Z, Yang Y, Chen S, Wu Z: Long-Term Exposure to Polystyrene Microspheres and High-Fat Diet\u0026ndash;Induced Obesity in Mice: Evaluating a Role for Microbiota Dysbiosis. ENVIRON HEALTH PERSP 2024, 132(9).\u003c/li\u003e\n\u003cli\u003eWang S, Huang XF, Zhang P, Wang H, Zhang Q, Yu S, Yu Y: Chronic rhein treatment improves recognition memory in high-fat diet-induced obese male mice. J NUTR BIOCHEM 2016, 36:42-50.\u003c/li\u003e\n\u003cli\u003eXiang X, Wang R, Chen L, Chen Y, Zheng B, Deng S, Liu S, Sun P, Shen G: Immunomodulatory activity of a water-soluble polysaccharide extracted from mussel on cyclophosphamide-induced immunosuppressive mice models. NPJ SCI FOOD 2022, 6(1):26.\u003c/li\u003e\n\u003cli\u003eYu Y, Yan P, Cheng G, Liu D, Xu L, Yang M, Xu H, Cheng X, Lian P, Zeng Y: Correlation between serum lipid profiles and cognitive impairment in old age: a cross-sectional study. GEN PSYCHIAT 2023, 36(2):e101009.\u003c/li\u003e\n\u003cli\u003eLiu Y, Zhong X, Shen J, Jiao L, Tong J, Zhao W, Du K, Gong S, Liu M, Wei M: Elevated serum TC and LDL-C levels in Alzheimer\u0026apos;s disease and mild cognitive impairment: A meta-analysis study. BRAIN RES 2020, 1727:146554.\u003c/li\u003e\n\u003cli\u003ePannerchelvan S, Rios-Solis L, Wasoh H, Sobri MZM, Faizal Wong FW, Mohamed MS, Mohamad R, Halim M: Functional yogurt: a comprehensive review of its nutritional composition and health benefits. FOOD FUNCT 2024, 15(22):1927-1955.\u003c/li\u003e\n\u003cli\u003eZhang X, Coker OO, Chu ES, Fu K, Lau HCH, Wang Y, Chan AWH, Wei H, Yang X, Sung JJY et al: Dietary cholesterol drives fatty liver-associated liver cancer by modulating gut microbiota and metabolites. GUT 2021, 70(4):761-774.\u003c/li\u003e\n\u003cli\u003eZhu L, Ying N, Hao L, Fu A, Ding Q, Cao F, Ren D, Han Q, Li S: Probiotic yogurt regulates gut microbiota homeostasis and alleviates hepatic steatosis and liver injury induced by high‐fat diet in golden hamsters. FOOD SCI NUTR 2024, 12(4):2488-2501.\u003c/li\u003e\n\u003cli\u003eYang Y, Yang L, Wu J, Hu J, Wan M, Bie J, Li J, Pan D, Sun G, Yang C: Optimal probiotic combinations for treating nonalcoholic fatty liver disease: A systematic review and network meta-analysis. Clinical nutrition (Edinburgh, Scotland) 2024, 43(6):1224-1239.\u003c/li\u003e\n\u003cli\u003eIto M, Kusuhara S, Yokoi W, Sato T, Ishiki H, Miida S, Matsui A, Nakamori K, Nonaka C, Miyazaki K: Streptococcus thermophilus fermented milk reduces serum MDA-LDL and blood pressure in healthy and mildly hypercholesterolaemic adults. BENEF MICROBES 2017, 8(2):171-178.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diet, Gut microbiota, Mild cognitive impairment, Low-density lipoprotein cholesterol","lastPublishedDoi":"10.21203/rs.3.rs-7011889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7011889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eProspective studies investigating the relationship between dietary factors and cognitive function in elderly Chinese populations remain limited. And the role of gut microbiota in this relationship is unclear. In this study, we aimed to explore the role of gut microbiota in the dietary modulation on mild cognitive impairment (MCI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study enrolled 48 patients with MCI and 43 age-matched healthy controls (HC). Participant demographics and blood lipid levels were recorded. Dietary habits were assessed using a food frequency questionnaire (FFQ), and cognitive function was evaluated with the Montreal Cognitive Assessment (MoCA). Fecal samples were collected for 16S rRNA gene sequencing. Spearman\u0026rsquo;s correlation analysis was employed to examine correlations between gut microbiota and dietary intake, cognitive function, and low-density lipoprotein cholesterol (LDL-C).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCompared to HC, MCI subjects had significantly lower education levels and higher serum total cholesterol (TC) and LDL-C levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The MCI group also exhibited significantly reduced consumption of bean curd/tofu pudding, yogurt, freshwater fish, shrimp and crab, pine nuts, and pumpkin (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Significant enrichment of the genera \u003cem\u003eDesulfovibrio\u003c/em\u003e, \u003cem\u003eSutterella\u003c/em\u003e, UCG-003, \u003cem\u003enorank_f__norank_o__RF39\u003c/em\u003e, UCG-002, \u003cem\u003eF0332\u003c/em\u003e, \u003cem\u003ePhocea\u003c/em\u003e, \u003cem\u003enorank_f__norank_o__Elsterales\u003c/em\u003e, and \u003cem\u003eBryobacte\u003c/em\u003e was observed in the MCI group. Conversely, \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eAtopobium\u003c/em\u003e, \u003cem\u003eEubacterium_eligens_group\u003c/em\u003e, \u003cem\u003eRuminococcus_gnavus_group\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e were significantly decreased in MCI subjects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Spearman\u0026rsquo;s correlation analysis revealed significant positive associations between cognitive scores and the intake of yogurt, freshwater fish, shrimp and crab, and pine nuts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, \u003cem\u003eActinomyces\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e abundance correlated positively not only with the intake of freshwater fish and yogurt but also with cognitive performance. Conversely, UCG-003 and \u003cem\u003eDesulfovibrio\u003c/em\u003e abundance correlated negatively with the intake of shrimp and crab, yogurt, as well as with cognitive scores. Additionally, serum LDL-C levels correlated negatively with yogurt intake and cognitive scores.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eIn conclusion, intake of yogurt, freshwater fish, and shrimp and crab was positively associated with cognitive performance. Gut microbiota composition, particularly enrichment of \u003cem\u003eActinomyces\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e, may mediate the beneficial cognitive effects of these dietary components. Conversely, UCG-003 and \u003cem\u003eDesulfovibrio\u003c/em\u003e may exert detrimental effects on cognition. Notably, serum LDL-C levels may represent a mediating factor in the diet-cognition relationship.\u003c/p\u003e","manuscriptTitle":"Gut Microbiota Mediates Dietary Modulation of Mild Cognitive Impairment in the Elderly: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-27 11:02:40","doi":"10.21203/rs.3.rs-7011889/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5407f35-f6f9-4ea2-8a7a-5e53679b84c7","owner":[],"postedDate":"July 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T10:53:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-27 11:02:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7011889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7011889","identity":"rs-7011889","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}